library(gplots)
library(foreign)
library(car)
library(plotly)
library(ggpubr)
library(ggplot2)
library(reprex)
library(dplyr)
library(readxl)
library(readr)
library(rpart)
library(glmulti)
library(caret)
library(InformationValue)
library(rvest)
library(stringi)
library(stringr)
library(dplyr)
library(fmsb)
library(dlookr)
library(ggrepel)
library(rattle)
library(corrplot)
library(factoextra)
library(mclust)
library(FactoMineR)
library(tidyr)
library(GGally)
library(gridExtra)
library(grid)
library(FSelector)
library(mlbench)
library(RRF)
library(wsrf)
library(Boruta)
options(warn=-1)
options(repr.plot.width = 18, repr.plot.height = 10)
options(repr.matrix.max.rows=600, repr.matrix.max.cols=200)
datos <- read_excel("C:/Users/34625/Downloads/PROYECTOS DE TRABAJO/Yankel Carolina Sena/fwddatayankelsena/original_yankel.xlsx",sheet='Hoja12')
datos
| STATUS | CYCLES_BETWEEN_PET1_PET2 | GENDER | AGE | TNM_STAGE | DIFF_WBC | DIFF_RBC | DIFF_HB | DIFF_PLT | DIFF_CRP | DIFF_ALBUMIN | DIFF_LDH | DIFF_eGFR | DIFF_AST | DIFF_ALT | DIFF_K | DIFF_BGL | BMI | DIFF_BW | DIFF_SPLEEN_UPTAKE | DIFF_BM_UPTAKE | DIFF_LIVER_UPTAKE | DIFF_ESTIMATED_SPLEEN_VOL | DIAGNOSTIC | TREATMENT | ECOGPS | COMORBIDITIES | CTCNCI | ACTION_TAKEN_ | TIME_BETWEE_PET | dias | DIFF_SLR | DIFF_BMLR | OVERALL_TIME |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <chr> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| ALIVE | 3 | MALE | 54 | III | -1.83 | -0.64 | -2.20 | -82 | -0.56 | 0.98 | -271.0 | 23.7 | 0.30 | 13.20 | 0.10 | 12 | 22.90 | -4.00 | 0.02 | 0.12 | 0.44 | -80.9 | GINECOLOGICAL | ICI | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | SEVERE | DRUG INTERRUPTED | 2 | 25 | 0.029335236 | 0.109059370 | 2 |
| ALIVE | 2 | FEMALE | 79 | IV | -3.08 | 0.14 | 0.70 | -159 | -3.80 | 1.33 | 22.0 | -0.2 | 4.40 | 5.30 | 0.58 | -14 | 16.47 | 0.30 | -0.20 | -0.60 | -0.30 | 6.2 | HEAD AND NECK | ICI | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | DRUG INTERRUPTED | 4 | 0 | -0.156566790 | -0.046889612 | 50 |
| ALIVE | 3 | MALE | 60 | III | -3.18 | -0.50 | 3.54 | 22 | -0.06 | -0.20 | 64.2 | 4.6 | -2.60 | 1.10 | 0.33 | 12 | 20.15 | -8.10 | -0.12 | 0.34 | 0.27 | 9.1 | LUNG CANCER | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | DIABETES MELLITUS | MODERATE | DOSE REDUCED | 4 | 14 | -0.044698028 | -0.365688558 | 2 |
| ALIVE | 3 | MALE | 76 | II | -1.28 | 0.79 | 1.70 | 9 | 0.04 | -0.12 | -55.0 | -8.9 | -0.70 | -6.70 | 0.05 | -4 | 22.83 | 0.00 | 0.46 | -0.40 | 0.59 | -38.8 | RENAL CANCER | ICI | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | DOSE NOT CHANGED | 6 | 0 | -0.002886671 | 0.130612972 | 12 |
| ALIVE | 5 | FEMALE | 70 | II | 0.00 | -0.09 | -0.40 | -95 | -7.23 | 0.05 | 6.0 | -13.2 | 2.70 | 2.20 | 0.06 | -8 | 18.29 | -0.10 | -0.34 | -0.80 | -0.48 | -87.6 | MELANOMA | ICI | SYMPTOMATIC BUT AMBULATORY | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE NOT CHANGED | 6 | 0 | -0.095215760 | -0.252680247 | 12 |
| ALIVE | 4 | MALE | 54 | IV | 0.99 | 0.10 | 0.00 | -3 | 0.02 | -0.16 | -4.0 | -13.1 | 0.60 | 2.80 | -0.47 | -3 | 27.18 | -10.80 | 0.00 | -0.10 | -0.53 | 34.4 | LUNG CANCER | ICI | ASYMTOMATIC | CANCER RRECURRENCE | HIGHT SEVERE | DOSE NOT CHANGED | 6 | 363 | -0.167591661 | 0.072513788 | 18 |
| ALIVE | 2 | MALE | 60 | III | -1.48 | -1.23 | -1.90 | -60 | 0.02 | -0.46 | -16.0 | 14.1 | 5.20 | 0.60 | -0.26 | -12 | 22.76 | 1.40 | -0.07 | 0.11 | -0.17 | -85.0 | LUNG CANCER | ICI | ASYMTOMATIC | CHRONIC INFLAMMATION | NO SIDE EFFECTS | DOSE NOT CHANGED | 9 | 0 | 0.114290017 | 0.200158109 | 7 |
| ALIVE | 5 | MALE | 69 | III | -1.65 | 0.04 | 0.60 | -354 | 4.66 | 0.52 | -333.0 | -9.2 | -7.30 | -23.90 | -0.02 | -70 | 17.36 | -2.20 | 0.61 | -0.18 | 0.61 | 11.3 | HEAD AND NECK | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | DIABETES MELLITUS | MODERATE | DOSE NOT CHANGED | 9 | 95 | 0.223201621 | 0.314140976 | 16 |
| ALIVE | 6 | MALE | 64 | IV | 3.89 | -0.78 | -3.20 | 242 | 15.21 | -1.22 | -28.0 | 9.0 | -10.40 | -13.20 | -0.23 | 70 | 23.65 | -11.10 | 0.30 | 0.50 | 0.03 | 8.1 | HEAD AND NECK | ICI | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | SLIGTHLY SIDE EFFCTS | DOSE NOT CHANGED | 10 | 28 | 0.163665694 | 0.070517745 | 65 |
| ALIVE | 12 | MALE | 72 | III | -0.80 | -0.37 | -0.90 | -26 | -0.02 | -0.09 | -13.0 | -2.1 | 0.00 | -1.90 | 0.26 | 64 | 22.84 | -6.60 | 0.24 | 0.48 | 0.65 | -51.4 | HEAD AND NECK | ICI | ASYMTOMATIC | CHRONIC INFLAMMATION | NO SIDE EFFECTS | DOSE NOT CHANGED | 16 | 0 | -0.117647059 | 0.083556150 | 1 |
| ALIVE | 29 | MALE | 71 | III | -0.91 | 0.07 | 0.20 | 11 | 0.18 | 0.18 | 52.0 | -8.7 | -0.60 | -0.10 | 0.59 | 11 | 21.34 | 0.00 | -0.05 | 0.69 | 0.09 | -46.1 | HEAD AND NECK | ICI | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | ADDED OTHER TREATMEN | 21 | 0 | 0.312678365 | 0.619776214 | 41 |
| ALIVE | 6 | MALE | 67 | III | -2.67 | -0.86 | 2.30 | -4 | -0.36 | -0.72 | 69.0 | 16.9 | 5.00 | 3.80 | 0.05 | 11 | 20.29 | -1.10 | 0.20 | 0.70 | 0.52 | 66.9 | LUNG CANCER | ICI | ASYMTOMATIC | CANCER RRECURRENCE | NO SIDE EFFECTS | DOSE NOT CHANGED | 25 | 0 | -0.160987704 | -0.061448788 | 11 |
| ALIVE | 48 | MALE | 60 | III | 4.52 | 0.11 | -1.90 | -63 | 17.83 | -0.61 | -15.0 | -24.1 | -4.10 | -4.40 | -0.67 | 64 | 29.11 | -6.20 | 1.13 | 2.08 | 0.31 | 177.0 | RENAL CANCER | ICI | ASYMTOMATIC | HYPERLIPIDEMIA | SLIGTHLY SIDE EFFCTS | UNKNOW | 25 | 735 | 0.413640470 | -0.037127371 | 11 |
| ALIVE | 20 | MALE | 61 | IV | 1.71 | 0.89 | 3.70 | -183 | -1.24 | 1.74 | 86.0 | -9.4 | -0.50 | 1.80 | -0.17 | -11 | 20.12 | 0.00 | 0.15 | -0.22 | 0.57 | -44.5 | MELANOMA | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | DIABETES MELLITUS | NO SIDE EFFECTS | UNKNOW | 38 | 0 | 0.125985126 | 0.396964147 | 15 |
| DEATH | 2 | MALE | 83 | II | 2.37 | -0.12 | 1.60 | 44 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.00 | 14 | 24.46 | -1.50 | 0.10 | 0.35 | -0.03 | 31.6 | MELANOMA | ICI | ASYMTOMATIC | CANCER RRECURRENCE | NO SIDE EFFECTS | DOSE NOT CHANGED | 11 | 0 | 0.253714576 | 0.288722186 | 4 |
| DEATH | 3 | MALE | 68 | I | 3.22 | -1.11 | 0.90 | -59 | 3.02 | -0.09 | 5.0 | -19.6 | -11.60 | -7.50 | 0.17 | 6 | 24.41 | -4.30 | -0.05 | -0.45 | 0.27 | -2.1 | MELANOMA | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | CANCER RRECURRENCE | NO SIDE EFFECTS | DOSE NOT CHANGED | 2 | 0 | 0.207638889 | 0.081944444 | 13 |
| DEATH | 18 | MALE | 77 | II | 0.37 | 0.63 | 1.90 | -24 | 0.22 | -0.06 | 46.0 | 7.5 | 6.50 | 4.10 | 0.48 | -12 | 18.07 | 0.00 | -0.13 | 0.20 | -0.14 | -2.4 | RENAL CANCER | ICI | ASYMTOMATIC | CHRONIC INFLAMMATION | SEVERE | ADDED STEROIDS | 10 | 3 | -0.117647059 | 0.083556150 | 12 |
| DEATH | 4 | MALE | 60 | III | -1.23 | -0.28 | -1.20 | -11 | 2.69 | 0.10 | 194.0 | -10.2 | 10.30 | 7.50 | -0.01 | 1 | 23.14 | 4.00 | -0.42 | -0.40 | -0.74 | 55.2 | LUNG CANCER | ICI | SYMPTOMATIC BUT AMBULATORY | CHRONIC INFLAMMATION | MODERATE | UNKNOW | 15 | 441 | 0.030665281 | 0.133264033 | 29 |
| DEATH | 7 | MALE | 64 | IV | 0.46 | -0.41 | -1.50 | 34 | 0.31 | -0.44 | -8.0 | -4.6 | -1.10 | 0.40 | 0.44 | -6 | 22.12 | 5.70 | 0.27 | 0.46 | -0.37 | 15.1 | HEAD AND NECK | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | DIABETES MELLITUS | MODERATE | DRUG INTERRUPTED | 4 | 52 | 0.135997522 | 0.200123916 | 42 |
| DEATH | 15 | MALE | 67 | II | -4.64 | 0.71 | 2.40 | -189 | -15.88 | 1.57 | -510.0 | -24.5 | -2.90 | -16.90 | 0.00 | -27 | 14.08 | 44.02 | -0.40 | -0.19 | 0.19 | 2.4 | HEAD AND NECK | ICI | SYMPTOMATIC BUT AMBULATORY | DIABETES MELLITUS | SLIGTHLY SIDE EFFCTS | DRUG INTERRUPTED | 13 | 364 | -0.513598150 | -0.140500210 | 21 |
| DEATH | 5 | MALE | 83 | IV | 1.13 | -0.21 | -0.60 | 16 | 0.32 | 0.08 | -9.0 | -2.8 | 0.70 | 0.40 | 0.59 | 16 | 20.07 | 5.90 | 0.22 | -0.21 | 0.34 | -85.5 | HEAD AND NECK | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | HYPERTENSION | NO SIDE EFFECTS | DOSE NOT CHANGED | 8 | 0 | -0.193627043 | -0.093738849 | 21 |
| DEATH | 4 | FEMALE | 56 | II | 7.74 | -0.33 | -0.80 | 463 | -2.03 | 2.83 | -140.0 | 45.3 | -219.60 | -250.80 | -0.90 | -18 | 21.79 | -2.30 | -0.16 | 0.26 | -0.84 | 34.0 | MELANOMA | ICI | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | HIGHT SEVERE | DOSE REDUCED | 9 | 233 | -0.045342594 | 0.034875129 | 8 |
| ALIVE | 7 | MALE | 65 | II | -1.68 | -0.68 | 1.00 | 0 | 7.55 | -0.13 | 8.0 | -3.3 | -6.90 | -5.70 | 0.04 | 14 | 18.18 | 12.20 | 0.31 | -0.54 | -0.85 | 22.3 | PANCREAS CANCER | CHEMO | ASYMTOMATIC | DIABETES MELLITUS | NO SIDE EFFECTS | UNKNOW | 1 | 0 | 0.004147110 | 0.162187359 | 27 |
| ALIVE | 5 | FEMALE | 55 | IV | -3.77 | -1.93 | 1.40 | -58 | 0.12 | 0.01 | -29.0 | 6.7 | -3.10 | -10.20 | 0.48 | 25 | 25.60 | 4.60 | 0.40 | 0.59 | -0.24 | 29.9 | GINECOLOGICAL | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | HYPERLIPIDEMIA | SEVERE | DRUG INTERRUPTED | 2 | 20 | -0.230769231 | -0.330769231 | 10 |
| ALIVE | 2 | FEMALE | 75 | III | -2.17 | -0.94 | -2.30 | -100 | 0.00 | 0.27 | 21.0 | -10.4 | 1.80 | -2.40 | -0.20 | 8 | 25.96 | -5.40 | 0.19 | 0.65 | 0.88 | -1.0 | LUNG CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | HYPERTENSION | MODERATE | DOSE REDUCED | 2 | 15 | 0.071091909 | 0.511586803 | 41 |
| ALIVE | 6 | MALE | 74 | III | -3.24 | -1.16 | -0.20 | 100 | -0.46 | -0.56 | 41.0 | 3.0 | 4.40 | 0.80 | 0.68 | 9 | 20.85 | 9.30 | 0.00 | -0.20 | 0.60 | 19.0 | PANCREAS CANCER | CHEMO | ASYMTOMATIC | HYPERTENSION | MODERATE | DOSE REDUCED | 3 | 35 | 0.509097867 | 0.149792993 | 3 |
| ALIVE | 3 | FEMALE | 76 | IV | -0.04 | -0.43 | -0.80 | 95 | 0.02 | -0.10 | 103.0 | -2.2 | 7.90 | 0.10 | -0.33 | 2 | 20.90 | -2.40 | -0.12 | 0.84 | -0.27 | 19.7 | LUNG CANCER | CHEMO | ASYMTOMATIC | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE NOT CHANGED | 3 | 0 | 0.123854660 | 0.028606149 | 32 |
| ALIVE | 3 | MALE | 77 | III | -5.70 | -1.29 | -2.60 | -229 | 0.18 | -0.21 | 45.0 | -9.7 | -19.70 | -84.70 | -0.10 | -13 | 22.94 | 0.00 | 0.03 | -0.33 | 0.84 | 98.8 | LUNG CANCER | CHEMO | ASYMTOMATIC | HYPERTENSION | HIGHT SEVERE | DOSE REDUCED | 3 | 6 | 0.047663153 | -0.219056440 | 15 |
| ALIVE | 4 | MALE | 68 | II | -0.47 | 0.05 | 5.80 | 69 | -2.74 | 1.18 | 67.0 | -16.2 | 1.20 | -0.70 | 0.76 | -20 | 21.57 | 5.40 | 0.26 | 0.55 | 0.46 | 4.1 | LUNG CANCER | CHEMO | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | DOSE NOT CHANGED | 4 | 0 | -0.026468254 | 0.043273810 | 14 |
| ALIVE | 2 | MALE | 68 | II | -2.47 | -0.73 | -1.00 | -61 | -0.10 | -0.12 | 25.0 | 13.4 | -0.70 | -2.10 | 0.19 | -13 | 35.81 | -1.30 | -0.05 | 0.43 | -0.07 | 15.1 | LUNG CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE NOT CHANGED | 4 | 0 | -0.318764676 | -0.090749584 | 15 |
| ALIVE | 3 | FEMALE | 86 | IV | -4.78 | -1.30 | -3.50 | -17 | -6.38 | -0.12 | -2.0 | -3.0 | -15.20 | -19.30 | 0.23 | -8 | 26.75 | -2.40 | 0.30 | 0.19 | 0.35 | 41.0 | UROTHELIAL CARCINOMA | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | HYPERTENSION | SEVERE | DOSE REDUCED | 5 | 12 | 0.243005526 | 0.290715505 | 24 |
| ALIVE | 2 | MALE | 74 | II | 1.13 | -0.30 | -1.70 | -32 | 0.27 | -0.25 | -12.0 | 1.2 | -5.60 | -5.60 | -0.07 | 21 | 19.62 | 15.60 | 0.20 | -0.30 | -0.10 | 56.3 | PANCREAS CANCER | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | HYPERLIPIDEMIA | SEVERE | DOSE REDUCED | 5 | 0 | -0.005084746 | -0.015906128 | 6 |
| ALIVE | 3 | FEMALE | 52 | I | 3.14 | 0.03 | 0.40 | -9 | 0.07 | 0.05 | 91.0 | 8.9 | 4.30 | 16.80 | 0.09 | 9 | 22.19 | -4.80 | -0.37 | 0.77 | -0.08 | 42.4 | EWING SARCOMA | CHEMO | ASYMTOMATIC | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE NOT CHANGED | 5 | 0 | 0.087355212 | -0.170045045 | 32 |
| ALIVE | 4 | FEMALE | 74 | IV | 2.19 | 0.44 | 1.30 | 59 | -0.01 | -0.24 | 40.0 | 1.9 | -3.20 | -8.60 | 0.14 | 24 | 17.78 | 5.60 | -0.16 | -0.01 | -0.23 | -1.8 | HEAD AND NECK | CHEMO | SYMPTOMATIC BUT AMBULATORY | HYPERTENSION | MODERATE | DOSE NOT CHANGED | 6 | 1 | 0.105820106 | -0.078042328 | 23 |
| ALIVE | 13 | FEMALE | 70 | IV | -1.92 | -0.90 | -1.60 | -233 | -1.94 | 1.15 | -294.0 | -3.3 | -18.70 | -6.40 | -0.19 | 20 | 24.33 | -1.20 | 0.08 | -1.01 | 0.22 | -55.6 | GINECOLOGICAL | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | HYPERLIPIDEMIA | NO SIDE EFFECTS | UNKNOW | 7 | 0 | 0.083963069 | 0.159957950 | 15 |
| ALIVE | 2 | MALE | 62 | III | 1.53 | -0.61 | -0.40 | -19 | -0.05 | -0.21 | -5.0 | -12.8 | 0.10 | -4.20 | 0.88 | 19 | 16.24 | 0.80 | -0.08 | 0.08 | 0.01 | 5.8 | LUNG CANCER | CHEMO | ASYMTOMATIC | CHRONIC INFLAMMATION | NO SIDE EFFECTS | DOSE NOT CHANGED | 8 | 0 | -0.244661571 | -0.313686865 | 63 |
| ALIVE | 3 | MALE | 79 | IV | 4.23 | 0.38 | 1.20 | 170 | 0.59 | -0.50 | -66.0 | 3.3 | -111.50 | -18.30 | 0.24 | -118 | 20.64 | 8.90 | -0.66 | -0.76 | -1.59 | -9.1 | RENAL CANCER | CHEMO | ASYMTOMATIC | CHRONIC INFLAMMATION | NO SIDE EFFECTS | DOSE NOT CHANGED | 9 | 0 | 0.202321083 | 0.496711799 | 9 |
| ALIVE | 14 | FEMALE | 69 | II | 3.39 | -1.31 | -2.20 | -59 | -0.09 | -0.60 | 16.0 | 34.3 | -4.70 | -13.80 | 0.10 | -41 | 18.57 | 0.80 | -0.39 | 0.06 | -0.70 | 99.4 | PANCREAS CANCER | CHEMO | SYMPTOMATIC >50 % IN THE BED | ATEROESCLEROSIS | NO SIDE EFFECTS | DOSE REDUCED | 9 | 0 | 0.061267406 | -0.157317420 | 4 |
| ALIVE | 10 | FEMALE | 65 | II | -2.68 | 0.18 | 1.80 | 316 | 4.78 | -1.70 | 14.0 | 47.7 | 4.70 | -1.20 | -0.56 | 15 | 16.51 | 15.10 | 0.22 | -0.36 | 0.02 | 46.9 | GINECOLOGICAL | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | HYPERLIPIDEMIA | SEVERE | DOSE REDUCED | 10 | 32 | -0.054315003 | -0.256317546 | 21 |
| ALIVE | 4 | MALE | 74 | III | 1.51 | 0.83 | 2.60 | -110 | -0.01 | 0.22 | -81.0 | -30.1 | -11.50 | -9.90 | 0.14 | -7 | 22.08 | -1.00 | 0.09 | -0.12 | 0.18 | -18.1 | LUNG CANCER | CHEMO | ASYMTOMATIC | HYPERTENSION | MODERATE | DOSE NOT CHANGED | 12 | 36 | -0.159000071 | -0.136205070 | 3 |
| ALIVE | 12 | MALE | 76 | III | -0.49 | -0.11 | -0.40 | -56 | 0.04 | -0.01 | 31.0 | -13.4 | 7.20 | 5.20 | 0.36 | -56 | 26.80 | -2.20 | 0.24 | 0.44 | 0.63 | -110.2 | LUNG CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | ATEROESCLEROSIS | SEVERE | ADDED OTHER TREATMEN | 14 | 51 | -0.040974878 | 0.214845830 | 6 |
| ALIVE | 4 | FEMALE | 72 | III | 2.00 | 0.84 | 2.40 | -16 | -0.01 | 0.22 | 1.0 | 1.4 | -6.70 | -4.20 | 0.06 | 2 | 15.43 | 4.50 | -0.80 | 0.90 | 0.14 | 18.0 | EWING SARCOMA | CHEMO | ASYMTOMATIC | CANCER RRECURRENCE | NO SIDE EFFECTS | ADDED OTHER TREATMEN | 17 | 0 | -0.292250233 | -0.619234360 | 20 |
| ALIVE | 14 | FEMALE | 76 | III | 3.86 | 1.15 | 3.80 | 15 | -3.88 | -1.50 | 30.0 | -7.6 | 18.90 | 18.30 | -0.10 | -23 | 29.56 | 3.60 | 0.66 | 0.00 | 0.50 | 26.7 | GINECOLOGICAL | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | HYPERTENSION | NO SIDE EFFECTS | UNKNOW | 19 | 0 | -0.314585877 | -0.182208995 | 20 |
| ALIVE | 4 | FEMALE | 56 | III | -3.11 | -0.22 | -0.50 | 4 | -0.10 | -0.08 | 9.0 | -20.0 | -2.90 | -1.60 | -0.03 | 43 | 18.03 | -1.30 | -0.63 | 0.21 | 0.49 | 2.6 | LUNG CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | DIABETES MELLITUS | NO SIDE EFFECTS | UNKNOW | 20 | 0 | 0.027777778 | -0.125000000 | 1 |
| ALIVE | 4 | MALE | 70 | III | -0.19 | -0.44 | -0.70 | -17 | -0.27 | 0.13 | 4.0 | -5.7 | -7.60 | -12.00 | 0.36 | -160 | 23.75 | -5.60 | -0.55 | -1.20 | 0.10 | 31.0 | LUNG CANCER | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | CHRONIC INFLAMMATION | NO SIDE EFFECTS | DOSE NOT CHANGED | 20 | 0 | -0.095525727 | -0.514605305 | 3 |
| ALIVE | 4 | FEMALE | 75 | III | -2.30 | 0.14 | 0.40 | -82 | 0.02 | -0.31 | 16.0 | -25.1 | -6.10 | -9.40 | 0.73 | 0 | 25.78 | -4.90 | 0.10 | 0.20 | -0.29 | 2.4 | GINECOLOGICAL | CHEMO | ASYMTOMATIC | HYPERTENSION | SEVERE | DOSE REDUCED | 33 | 312 | 0.048263534 | 0.141343207 | 5 |
| DEATH | 9 | MALE | 81 | II | 0.10 | -0.19 | -1.10 | 1 | 1.19 | -0.06 | -4.0 | -2.2 | -5.50 | -8.80 | 1.07 | 16 | 16.17 | 0.00 | 0.34 | -0.82 | -0.65 | -68.5 | GASTRIC CANCER | CHEMO | ASYMTOMATIC | CANCER RRECURRENCE | MODERATE | DRUG INTERRUPTED | 9 | 86 | 0.226870727 | 0.438938719 | 22 |
| DEATH | 9 | MALE | 73 | III | -1.94 | 0.13 | 0.40 | 75 | -1.67 | -2.28 | 32.0 | -32.7 | 15.60 | -2.40 | 0.51 | -1 | 16.90 | 26.20 | 0.20 | 0.70 | 0.52 | -3.1 | PANCREAS CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | SEVERE | DRUG INTERRUPTED | 4 | 195 | 0.065394796 | 0.097268444 | 16 |
| DEATH | 5 | MALE | 77 | I | -2.19 | -1.97 | -5.20 | -151 | 0.39 | 0.12 | 34.0 | 3.8 | 11.90 | 19.40 | -0.64 | 30 | 25.21 | 5.30 | -0.14 | -0.72 | 0.30 | -7.6 | LUNG CANCER | CHEMO | ASYMTOMATIC | CHRONIC INFLAMMATION | NO SIDE EFFECTS | DRUG WIHDRAWN | 5 | 0 | -0.080303030 | -0.620707071 | 9 |
| DEATH | 6 | MALE | 74 | I | 5.48 | -0.77 | -2.80 | 15 | 7.21 | -0.37 | 472.0 | 6.8 | -1.10 | -2.60 | 0.35 | 29 | 23.51 | 7.50 | 0.34 | 0.01 | -0.32 | -5.6 | GASTRIC CANCER | CHEMO | ASYMTOMATIC | CANCER RRECURRENCE | NO SIDE EFFECTS | DOSE NOT CHANGED | 12 | 0 | -0.022968112 | -0.097195217 | 1 |
| DEATH | 6 | MALE | 70 | I | 0.38 | 0.59 | 0.20 | -9 | 0.25 | -0.05 | 18.0 | -6.3 | -0.80 | -8.60 | 0.63 | 17 | 16.68 | 18.70 | -0.07 | 0.09 | -0.01 | 33.1 | LUNG CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | SEVERE | DOSE REDUCED | 12 | 50 | -0.136822783 | -0.321107069 | 13 |
| DEATH | 6 | MALE | 63 | II | 2.42 | 0.49 | 4.00 | 26 | 2.96 | -1.35 | 170.0 | 9.9 | -55.90 | -84.90 | -0.34 | -70 | 17.89 | 24.10 | -0.06 | 0.21 | -0.25 | 84.2 | PANCREAS CANCER | CHEMO | SYMPTOMATIC,<50% IN BED DURING THE DAY | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE REDUCED | 7 | 0 | -0.195751634 | -0.070728291 | 64 |
| DEATH | 5 | FEMALE | 61 | II | -0.96 | -1.44 | -2.70 | -12 | 0.03 | -0.12 | 15.0 | -0.9 | 6.60 | 14.30 | 0.91 | 29 | 20.44 | 6.90 | -0.41 | 0.24 | 0.51 | 34.2 | GINECOLOGICAL | CHEMO | ASYMTOMATIC | HYPERLIPIDEMIA | HIGHT SEVERE | DOSE REDUCED | 11 | 114 | -0.101647059 | 0.002627451 | 12 |
| DEATH | 2 | MALE | 79 | III | -2.67 | -0.86 | -3.50 | 4 | 1.03 | -0.90 | -34.0 | -39.2 | -6.50 | -6.00 | 0.73 | 1 | 17.30 | 4.80 | 0.05 | 0.74 | -0.34 | -20.2 | LUNG CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | CANCER RRECURRENCE | SEVERE | DOSE REDUCED | 3 | 0 | -0.502580577 | 0.369233200 | 17 |
| DEATH | 3 | FEMALE | 68 | III | -1.29 | -0.52 | -1.60 | 79 | -0.02 | 0.05 | -23.0 | 9.5 | -5.80 | -9.20 | 0.14 | 17 | 25.78 | 3.10 | 1.13 | 1.83 | -0.87 | 18.5 | SARCOMA | CHEMO | ASYMTOMATIC | ATEROESCLEROSIS | MODERATE | DRUG INTERRUPTED | 9 | 7 | 0.688749725 | 1.020032227 | 16 |
| DEATH | 6 | FEMALE | 67 | III | -2.68 | -1.02 | 0.60 | -258 | -0.22 | -0.17 | -5.0 | -2.6 | -0.01 | 0.62 | 0.25 | -6 | 19.07 | 8.70 | 0.12 | 0.39 | 0.77 | 2.7 | HEAD AND NECK | CHEMO | SYMPTOMATIC BUT AMBULATORY | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE REDUCED | 1 | 0 | 0.217516526 | 0.289366507 | 47 |
| DEATH | 10 | MALE | 87 | III | 2.06 | -0.29 | -1.00 | 197 | -2.64 | 0.20 | -65.0 | 9.3 | 13.20 | 3.30 | -0.28 | -1 | 23.76 | 6.60 | 0.05 | -0.45 | -0.38 | 65.2 | GASTRIC CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | HYPERTENSION | SEVERE | DOSE REDUCED | 10 | 33 | -0.256666667 | -0.538333333 | 30 |
| DEATH | 4 | MALE | 78 | III | -0.31 | -0.29 | -0.90 | 31 | -0.15 | -0.16 | 5.0 | -26.4 | -3.50 | -10.70 | 0.48 | -2 | 19.86 | 3.60 | 0.33 | 0.41 | -0.35 | 38.9 | LUNG CANCER | CHEMO | BEDBOUND | CHRONIC INFLAMMATION | MODERATE | DRUG INTERRUPTED | 8 | 7 | -0.135169763 | 0.376817502 | 32 |
| DEATH | 8 | FEMALE | 53 | I | -0.36 | -0.45 | -1.20 | -40 | 0.39 | -0.11 | 5.0 | -5.9 | 1.70 | -3.00 | 0.05 | 18 | 19.26 | 4.60 | 0.08 | -0.13 | -0.16 | 24.0 | PANCREAS CANCER | CHEMO | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | SLIGTHLY SIDE EFFCTS | DOSE REDUCED | 3 | 7 | 0.162247681 | -0.019717871 | 23 |
datos<-as.data.frame(datos)
datos
| STATUS | CYCLES_BETWEEN_PET1_PET2 | GENDER | AGE | TNM_STAGE | DIFF_WBC | DIFF_RBC | DIFF_HB | DIFF_PLT | DIFF_CRP | DIFF_ALBUMIN | DIFF_LDH | DIFF_eGFR | DIFF_AST | DIFF_ALT | DIFF_K | DIFF_BGL | BMI | DIFF_BW | DIFF_SPLEEN_UPTAKE | DIFF_BM_UPTAKE | DIFF_LIVER_UPTAKE | DIFF_ESTIMATED_SPLEEN_VOL | DIAGNOSTIC | TREATMENT | ECOGPS | COMORBIDITIES | CTCNCI | ACTION_TAKEN_ | TIME_BETWEE_PET | dias | DIFF_SLR | DIFF_BMLR | OVERALL_TIME |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| 1 | 3 | 2 | 54 | 3 | -1.83 | -0.64 | -2.20 | -82 | -0.56 | 0.98 | -271.0 | 23.7 | 0.30 | 13.20 | 0.10 | 12 | 22.90 | -4.00 | 0.02 | 0.12 | 0.44 | -80.9 | 3 | 2 | 4 | 5 | 4 | 5 | 2 | 25 | 0.029335236 | 0.109059370 | 2 |
| 1 | 2 | 1 | 79 | 4 | -3.08 | 0.14 | 0.70 | -159 | -3.80 | 1.33 | 22.0 | -0.2 | 4.40 | 5.30 | 0.58 | -14 | 16.47 | 0.30 | -0.20 | -0.60 | -0.30 | 6.2 | 4 | 2 | 1 | 6 | 3 | 5 | 4 | 0 | -0.156566790 | -0.046889612 | 50 |
| 1 | 3 | 2 | 60 | 3 | -3.18 | -0.50 | 3.54 | 22 | -0.06 | -0.20 | 64.2 | 4.6 | -2.60 | 1.10 | 0.33 | 12 | 20.15 | -8.10 | -0.12 | 0.34 | 0.27 | 9.1 | 5 | 2 | 5 | 4 | 2 | 4 | 4 | 14 | -0.044698028 | -0.365688558 | 2 |
| 1 | 3 | 2 | 76 | 2 | -1.28 | 0.79 | 1.70 | 9 | 0.04 | -0.12 | -55.0 | -8.9 | -0.70 | -6.70 | 0.05 | -4 | 22.83 | 0.00 | 0.46 | -0.40 | 0.59 | -38.8 | 8 | 2 | 1 | 6 | 3 | 3 | 6 | 0 | -0.002886671 | 0.130612972 | 12 |
| 1 | 5 | 1 | 70 | 2 | 0.00 | -0.09 | -0.40 | -95 | -7.23 | 0.05 | 6.0 | -13.2 | 2.70 | 2.20 | 0.06 | -8 | 18.29 | -0.10 | -0.34 | -0.80 | -0.48 | -87.6 | 6 | 2 | 4 | 4 | 3 | 3 | 6 | 0 | -0.095215760 | -0.252680247 | 12 |
| 1 | 4 | 2 | 54 | 4 | 0.99 | 0.10 | 0.00 | -3 | 0.02 | -0.16 | -4.0 | -13.1 | 0.60 | 2.80 | -0.47 | -3 | 27.18 | -10.80 | 0.00 | -0.10 | -0.53 | 34.4 | 5 | 2 | 1 | 2 | 1 | 3 | 6 | 363 | -0.167591661 | 0.072513788 | 18 |
| 1 | 2 | 2 | 60 | 3 | -1.48 | -1.23 | -1.90 | -60 | 0.02 | -0.46 | -16.0 | 14.1 | 5.20 | 0.60 | -0.26 | -12 | 22.76 | 1.40 | -0.07 | 0.11 | -0.17 | -85.0 | 5 | 2 | 1 | 3 | 3 | 3 | 9 | 0 | 0.114290017 | 0.200158109 | 7 |
| 1 | 5 | 2 | 69 | 3 | -1.65 | 0.04 | 0.60 | -354 | 4.66 | 0.52 | -333.0 | -9.2 | -7.30 | -23.90 | -0.02 | -70 | 17.36 | -2.20 | 0.61 | -0.18 | 0.61 | 11.3 | 4 | 2 | 5 | 4 | 2 | 3 | 9 | 95 | 0.223201621 | 0.314140976 | 16 |
| 1 | 6 | 2 | 64 | 4 | 3.89 | -0.78 | -3.20 | 242 | 15.21 | -1.22 | -28.0 | 9.0 | -10.40 | -13.20 | -0.23 | 70 | 23.65 | -11.10 | 0.30 | 0.50 | 0.03 | 8.1 | 4 | 2 | 4 | 5 | 5 | 3 | 10 | 28 | 0.163665694 | 0.070517745 | 65 |
| 1 | 12 | 2 | 72 | 3 | -0.80 | -0.37 | -0.90 | -26 | -0.02 | -0.09 | -13.0 | -2.1 | 0.00 | -1.90 | 0.26 | 64 | 22.84 | -6.60 | 0.24 | 0.48 | 0.65 | -51.4 | 4 | 2 | 1 | 3 | 3 | 3 | 16 | 0 | -0.117647059 | 0.083556150 | 1 |
| 1 | 29 | 2 | 71 | 3 | -0.91 | 0.07 | 0.20 | 11 | 0.18 | 0.18 | 52.0 | -8.7 | -0.60 | -0.10 | 0.59 | 11 | 21.34 | 0.00 | -0.05 | 0.69 | 0.09 | -46.1 | 4 | 2 | 1 | 6 | 3 | 1 | 21 | 0 | 0.312678365 | 0.619776214 | 41 |
| 1 | 6 | 2 | 67 | 3 | -2.67 | -0.86 | 2.30 | -4 | -0.36 | -0.72 | 69.0 | 16.9 | 5.00 | 3.80 | 0.05 | 11 | 20.29 | -1.10 | 0.20 | 0.70 | 0.52 | 66.9 | 5 | 2 | 1 | 2 | 3 | 3 | 25 | 0 | -0.160987704 | -0.061448788 | 11 |
| 1 | 48 | 2 | 60 | 3 | 4.52 | 0.11 | -1.90 | -63 | 17.83 | -0.61 | -15.0 | -24.1 | -4.10 | -4.40 | -0.67 | 64 | 29.11 | -6.20 | 1.13 | 2.08 | 0.31 | 177.0 | 8 | 2 | 1 | 5 | 5 | 7 | 25 | 735 | 0.413640470 | -0.037127371 | 11 |
| 1 | 20 | 2 | 61 | 4 | 1.71 | 0.89 | 3.70 | -183 | -1.24 | 1.74 | 86.0 | -9.4 | -0.50 | 1.80 | -0.17 | -11 | 20.12 | 0.00 | 0.15 | -0.22 | 0.57 | -44.5 | 6 | 2 | 5 | 4 | 3 | 7 | 38 | 0 | 0.125985126 | 0.396964147 | 15 |
| 2 | 2 | 2 | 83 | 2 | 2.37 | -0.12 | 1.60 | 44 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.00 | 14 | 24.46 | -1.50 | 0.10 | 0.35 | -0.03 | 31.6 | 6 | 2 | 1 | 2 | 3 | 3 | 11 | 0 | 0.253714576 | 0.288722186 | 4 |
| 2 | 3 | 2 | 68 | 1 | 3.22 | -1.11 | 0.90 | -59 | 3.02 | -0.09 | 5.0 | -19.6 | -11.60 | -7.50 | 0.17 | 6 | 24.41 | -4.30 | -0.05 | -0.45 | 0.27 | -2.1 | 6 | 2 | 5 | 2 | 3 | 3 | 2 | 0 | 0.207638889 | 0.081944444 | 13 |
| 2 | 18 | 2 | 77 | 2 | 0.37 | 0.63 | 1.90 | -24 | 0.22 | -0.06 | 46.0 | 7.5 | 6.50 | 4.10 | 0.48 | -12 | 18.07 | 0.00 | -0.13 | 0.20 | -0.14 | -2.4 | 8 | 2 | 1 | 3 | 4 | 2 | 10 | 3 | -0.117647059 | 0.083556150 | 12 |
| 2 | 4 | 2 | 60 | 3 | -1.23 | -0.28 | -1.20 | -11 | 2.69 | 0.10 | 194.0 | -10.2 | 10.30 | 7.50 | -0.01 | 1 | 23.14 | 4.00 | -0.42 | -0.40 | -0.74 | 55.2 | 5 | 2 | 4 | 3 | 2 | 7 | 15 | 441 | 0.030665281 | 0.133264033 | 29 |
| 2 | 7 | 2 | 64 | 4 | 0.46 | -0.41 | -1.50 | 34 | 0.31 | -0.44 | -8.0 | -4.6 | -1.10 | 0.40 | 0.44 | -6 | 22.12 | 5.70 | 0.27 | 0.46 | -0.37 | 15.1 | 4 | 2 | 5 | 4 | 2 | 5 | 4 | 52 | 0.135997522 | 0.200123916 | 42 |
| 2 | 15 | 2 | 67 | 2 | -4.64 | 0.71 | 2.40 | -189 | -15.88 | 1.57 | -510.0 | -24.5 | -2.90 | -16.90 | 0.00 | -27 | 14.08 | 44.02 | -0.40 | -0.19 | 0.19 | 2.4 | 4 | 2 | 4 | 4 | 5 | 5 | 13 | 364 | -0.513598150 | -0.140500210 | 21 |
| 2 | 5 | 2 | 83 | 4 | 1.13 | -0.21 | -0.60 | 16 | 0.32 | 0.08 | -9.0 | -2.8 | 0.70 | 0.40 | 0.59 | 16 | 20.07 | 5.90 | 0.22 | -0.21 | 0.34 | -85.5 | 4 | 2 | 5 | 6 | 3 | 3 | 8 | 0 | -0.193627043 | -0.093738849 | 21 |
| 2 | 4 | 1 | 56 | 2 | 7.74 | -0.33 | -0.80 | 463 | -2.03 | 2.83 | -140.0 | 45.3 | -219.60 | -250.80 | -0.90 | -18 | 21.79 | -2.30 | -0.16 | 0.26 | -0.84 | 34.0 | 6 | 2 | 4 | 5 | 1 | 4 | 9 | 233 | -0.045342594 | 0.034875129 | 8 |
| 1 | 7 | 2 | 65 | 2 | -1.68 | -0.68 | 1.00 | 0 | 7.55 | -0.13 | 8.0 | -3.3 | -6.90 | -5.70 | 0.04 | 14 | 18.18 | 12.20 | 0.31 | -0.54 | -0.85 | 22.3 | 7 | 1 | 1 | 4 | 3 | 7 | 1 | 0 | 0.004147110 | 0.162187359 | 27 |
| 1 | 5 | 1 | 55 | 4 | -3.77 | -1.93 | 1.40 | -58 | 0.12 | 0.01 | -29.0 | 6.7 | -3.10 | -10.20 | 0.48 | 25 | 25.60 | 4.60 | 0.40 | 0.59 | -0.24 | 29.9 | 3 | 1 | 5 | 5 | 4 | 5 | 2 | 20 | -0.230769231 | -0.330769231 | 10 |
| 1 | 2 | 1 | 75 | 3 | -2.17 | -0.94 | -2.30 | -100 | 0.00 | 0.27 | 21.0 | -10.4 | 1.80 | -2.40 | -0.20 | 8 | 25.96 | -5.40 | 0.19 | 0.65 | 0.88 | -1.0 | 5 | 1 | 4 | 6 | 2 | 4 | 2 | 15 | 0.071091909 | 0.511586803 | 41 |
| 1 | 6 | 2 | 74 | 3 | -3.24 | -1.16 | -0.20 | 100 | -0.46 | -0.56 | 41.0 | 3.0 | 4.40 | 0.80 | 0.68 | 9 | 20.85 | 9.30 | 0.00 | -0.20 | 0.60 | 19.0 | 7 | 1 | 1 | 6 | 2 | 4 | 3 | 35 | 0.509097867 | 0.149792993 | 3 |
| 1 | 3 | 1 | 76 | 4 | -0.04 | -0.43 | -0.80 | 95 | 0.02 | -0.10 | 103.0 | -2.2 | 7.90 | 0.10 | -0.33 | 2 | 20.90 | -2.40 | -0.12 | 0.84 | -0.27 | 19.7 | 5 | 1 | 1 | 4 | 3 | 3 | 3 | 0 | 0.123854660 | 0.028606149 | 32 |
| 1 | 3 | 2 | 77 | 3 | -5.70 | -1.29 | -2.60 | -229 | 0.18 | -0.21 | 45.0 | -9.7 | -19.70 | -84.70 | -0.10 | -13 | 22.94 | 0.00 | 0.03 | -0.33 | 0.84 | 98.8 | 5 | 1 | 1 | 6 | 1 | 4 | 3 | 6 | 0.047663153 | -0.219056440 | 15 |
| 1 | 4 | 2 | 68 | 2 | -0.47 | 0.05 | 5.80 | 69 | -2.74 | 1.18 | 67.0 | -16.2 | 1.20 | -0.70 | 0.76 | -20 | 21.57 | 5.40 | 0.26 | 0.55 | 0.46 | 4.1 | 5 | 1 | 1 | 6 | 3 | 3 | 4 | 0 | -0.026468254 | 0.043273810 | 14 |
| 1 | 2 | 2 | 68 | 2 | -2.47 | -0.73 | -1.00 | -61 | -0.10 | -0.12 | 25.0 | 13.4 | -0.70 | -2.10 | 0.19 | -13 | 35.81 | -1.30 | -0.05 | 0.43 | -0.07 | 15.1 | 5 | 1 | 4 | 4 | 3 | 3 | 4 | 0 | -0.318764676 | -0.090749584 | 15 |
| 1 | 3 | 1 | 86 | 4 | -4.78 | -1.30 | -3.50 | -17 | -6.38 | -0.12 | -2.0 | -3.0 | -15.20 | -19.30 | 0.23 | -8 | 26.75 | -2.40 | 0.30 | 0.19 | 0.35 | 41.0 | 10 | 1 | 5 | 6 | 4 | 4 | 5 | 12 | 0.243005526 | 0.290715505 | 24 |
| 1 | 2 | 2 | 74 | 2 | 1.13 | -0.30 | -1.70 | -32 | 0.27 | -0.25 | -12.0 | 1.2 | -5.60 | -5.60 | -0.07 | 21 | 19.62 | 15.60 | 0.20 | -0.30 | -0.10 | 56.3 | 7 | 1 | 5 | 5 | 4 | 4 | 5 | 0 | -0.005084746 | -0.015906128 | 6 |
| 1 | 3 | 1 | 52 | 1 | 3.14 | 0.03 | 0.40 | -9 | 0.07 | 0.05 | 91.0 | 8.9 | 4.30 | 16.80 | 0.09 | 9 | 22.19 | -4.80 | -0.37 | 0.77 | -0.08 | 42.4 | 1 | 1 | 1 | 4 | 3 | 3 | 5 | 0 | 0.087355212 | -0.170045045 | 32 |
| 1 | 4 | 1 | 74 | 4 | 2.19 | 0.44 | 1.30 | 59 | -0.01 | -0.24 | 40.0 | 1.9 | -3.20 | -8.60 | 0.14 | 24 | 17.78 | 5.60 | -0.16 | -0.01 | -0.23 | -1.8 | 4 | 1 | 4 | 6 | 2 | 3 | 6 | 1 | 0.105820106 | -0.078042328 | 23 |
| 1 | 13 | 1 | 70 | 4 | -1.92 | -0.90 | -1.60 | -233 | -1.94 | 1.15 | -294.0 | -3.3 | -18.70 | -6.40 | -0.19 | 20 | 24.33 | -1.20 | 0.08 | -1.01 | 0.22 | -55.6 | 3 | 1 | 5 | 5 | 3 | 7 | 7 | 0 | 0.083963069 | 0.159957950 | 15 |
| 1 | 2 | 2 | 62 | 3 | 1.53 | -0.61 | -0.40 | -19 | -0.05 | -0.21 | -5.0 | -12.8 | 0.10 | -4.20 | 0.88 | 19 | 16.24 | 0.80 | -0.08 | 0.08 | 0.01 | 5.8 | 5 | 1 | 1 | 3 | 3 | 3 | 8 | 0 | -0.244661571 | -0.313686865 | 63 |
| 1 | 3 | 2 | 79 | 4 | 4.23 | 0.38 | 1.20 | 170 | 0.59 | -0.50 | -66.0 | 3.3 | -111.50 | -18.30 | 0.24 | -118 | 20.64 | 8.90 | -0.66 | -0.76 | -1.59 | -9.1 | 8 | 1 | 1 | 3 | 3 | 3 | 9 | 0 | 0.202321083 | 0.496711799 | 9 |
| 1 | 14 | 1 | 69 | 2 | 3.39 | -1.31 | -2.20 | -59 | -0.09 | -0.60 | 16.0 | 34.3 | -4.70 | -13.80 | 0.10 | -41 | 18.57 | 0.80 | -0.39 | 0.06 | -0.70 | 99.4 | 7 | 1 | 3 | 1 | 3 | 4 | 9 | 0 | 0.061267406 | -0.157317420 | 4 |
| 1 | 10 | 1 | 65 | 2 | -2.68 | 0.18 | 1.80 | 316 | 4.78 | -1.70 | 14.0 | 47.7 | 4.70 | -1.20 | -0.56 | 15 | 16.51 | 15.10 | 0.22 | -0.36 | 0.02 | 46.9 | 3 | 1 | 5 | 5 | 4 | 4 | 10 | 32 | -0.054315003 | -0.256317546 | 21 |
| 1 | 4 | 2 | 74 | 3 | 1.51 | 0.83 | 2.60 | -110 | -0.01 | 0.22 | -81.0 | -30.1 | -11.50 | -9.90 | 0.14 | -7 | 22.08 | -1.00 | 0.09 | -0.12 | 0.18 | -18.1 | 5 | 1 | 1 | 6 | 2 | 3 | 12 | 36 | -0.159000071 | -0.136205070 | 3 |
| 1 | 12 | 2 | 76 | 3 | -0.49 | -0.11 | -0.40 | -56 | 0.04 | -0.01 | 31.0 | -13.4 | 7.20 | 5.20 | 0.36 | -56 | 26.80 | -2.20 | 0.24 | 0.44 | 0.63 | -110.2 | 5 | 1 | 4 | 1 | 4 | 1 | 14 | 51 | -0.040974878 | 0.214845830 | 6 |
| 1 | 4 | 1 | 72 | 3 | 2.00 | 0.84 | 2.40 | -16 | -0.01 | 0.22 | 1.0 | 1.4 | -6.70 | -4.20 | 0.06 | 2 | 15.43 | 4.50 | -0.80 | 0.90 | 0.14 | 18.0 | 1 | 1 | 1 | 2 | 3 | 1 | 17 | 0 | -0.292250233 | -0.619234360 | 20 |
| 1 | 14 | 1 | 76 | 3 | 3.86 | 1.15 | 3.80 | 15 | -3.88 | -1.50 | 30.0 | -7.6 | 18.90 | 18.30 | -0.10 | -23 | 29.56 | 3.60 | 0.66 | 0.00 | 0.50 | 26.7 | 3 | 1 | 5 | 6 | 3 | 7 | 19 | 0 | -0.314585877 | -0.182208995 | 20 |
| 1 | 4 | 1 | 56 | 3 | -3.11 | -0.22 | -0.50 | 4 | -0.10 | -0.08 | 9.0 | -20.0 | -2.90 | -1.60 | -0.03 | 43 | 18.03 | -1.30 | -0.63 | 0.21 | 0.49 | 2.6 | 5 | 1 | 4 | 4 | 3 | 7 | 20 | 0 | 0.027777778 | -0.125000000 | 1 |
| 1 | 4 | 2 | 70 | 3 | -0.19 | -0.44 | -0.70 | -17 | -0.27 | 0.13 | 4.0 | -5.7 | -7.60 | -12.00 | 0.36 | -160 | 23.75 | -5.60 | -0.55 | -1.20 | 0.10 | 31.0 | 5 | 1 | 5 | 3 | 3 | 3 | 20 | 0 | -0.095525727 | -0.514605305 | 3 |
| 1 | 4 | 1 | 75 | 3 | -2.30 | 0.14 | 0.40 | -82 | 0.02 | -0.31 | 16.0 | -25.1 | -6.10 | -9.40 | 0.73 | 0 | 25.78 | -4.90 | 0.10 | 0.20 | -0.29 | 2.4 | 3 | 1 | 1 | 6 | 4 | 4 | 33 | 312 | 0.048263534 | 0.141343207 | 5 |
| 2 | 9 | 2 | 81 | 2 | 0.10 | -0.19 | -1.10 | 1 | 1.19 | -0.06 | -4.0 | -2.2 | -5.50 | -8.80 | 1.07 | 16 | 16.17 | 0.00 | 0.34 | -0.82 | -0.65 | -68.5 | 2 | 1 | 1 | 2 | 2 | 5 | 9 | 86 | 0.226870727 | 0.438938719 | 22 |
| 2 | 9 | 2 | 73 | 3 | -1.94 | 0.13 | 0.40 | 75 | -1.67 | -2.28 | 32.0 | -32.7 | 15.60 | -2.40 | 0.51 | -1 | 16.90 | 26.20 | 0.20 | 0.70 | 0.52 | -3.1 | 7 | 1 | 4 | 5 | 4 | 5 | 4 | 195 | 0.065394796 | 0.097268444 | 16 |
| 2 | 5 | 2 | 77 | 1 | -2.19 | -1.97 | -5.20 | -151 | 0.39 | 0.12 | 34.0 | 3.8 | 11.90 | 19.40 | -0.64 | 30 | 25.21 | 5.30 | -0.14 | -0.72 | 0.30 | -7.6 | 5 | 1 | 1 | 3 | 3 | 6 | 5 | 0 | -0.080303030 | -0.620707071 | 9 |
| 2 | 6 | 2 | 74 | 1 | 5.48 | -0.77 | -2.80 | 15 | 7.21 | -0.37 | 472.0 | 6.8 | -1.10 | -2.60 | 0.35 | 29 | 23.51 | 7.50 | 0.34 | 0.01 | -0.32 | -5.6 | 2 | 1 | 1 | 2 | 3 | 3 | 12 | 0 | -0.022968112 | -0.097195217 | 1 |
| 2 | 6 | 2 | 70 | 1 | 0.38 | 0.59 | 0.20 | -9 | 0.25 | -0.05 | 18.0 | -6.3 | -0.80 | -8.60 | 0.63 | 17 | 16.68 | 18.70 | -0.07 | 0.09 | -0.01 | 33.1 | 5 | 1 | 4 | 5 | 4 | 4 | 12 | 50 | -0.136822783 | -0.321107069 | 13 |
| 2 | 6 | 2 | 63 | 2 | 2.42 | 0.49 | 4.00 | 26 | 2.96 | -1.35 | 170.0 | 9.9 | -55.90 | -84.90 | -0.34 | -70 | 17.89 | 24.10 | -0.06 | 0.21 | -0.25 | 84.2 | 7 | 1 | 5 | 4 | 3 | 4 | 7 | 0 | -0.195751634 | -0.070728291 | 64 |
| 2 | 5 | 1 | 61 | 2 | -0.96 | -1.44 | -2.70 | -12 | 0.03 | -0.12 | 15.0 | -0.9 | 6.60 | 14.30 | 0.91 | 29 | 20.44 | 6.90 | -0.41 | 0.24 | 0.51 | 34.2 | 3 | 1 | 1 | 5 | 1 | 4 | 11 | 114 | -0.101647059 | 0.002627451 | 12 |
| 2 | 2 | 2 | 79 | 3 | -2.67 | -0.86 | -3.50 | 4 | 1.03 | -0.90 | -34.0 | -39.2 | -6.50 | -6.00 | 0.73 | 1 | 17.30 | 4.80 | 0.05 | 0.74 | -0.34 | -20.2 | 5 | 1 | 4 | 2 | 4 | 4 | 3 | 0 | -0.502580577 | 0.369233200 | 17 |
| 2 | 3 | 1 | 68 | 3 | -1.29 | -0.52 | -1.60 | 79 | -0.02 | 0.05 | -23.0 | 9.5 | -5.80 | -9.20 | 0.14 | 17 | 25.78 | 3.10 | 1.13 | 1.83 | -0.87 | 18.5 | 9 | 1 | 1 | 1 | 2 | 5 | 9 | 7 | 0.688749725 | 1.020032227 | 16 |
| 2 | 6 | 1 | 67 | 3 | -2.68 | -1.02 | 0.60 | -258 | -0.22 | -0.17 | -5.0 | -2.6 | -0.01 | 0.62 | 0.25 | -6 | 19.07 | 8.70 | 0.12 | 0.39 | 0.77 | 2.7 | 4 | 1 | 4 | 4 | 3 | 4 | 1 | 0 | 0.217516526 | 0.289366507 | 47 |
| 2 | 10 | 2 | 87 | 3 | 2.06 | -0.29 | -1.00 | 197 | -2.64 | 0.20 | -65.0 | 9.3 | 13.20 | 3.30 | -0.28 | -1 | 23.76 | 6.60 | 0.05 | -0.45 | -0.38 | 65.2 | 2 | 1 | 4 | 6 | 4 | 4 | 10 | 33 | -0.256666667 | -0.538333333 | 30 |
| 2 | 4 | 2 | 78 | 3 | -0.31 | -0.29 | -0.90 | 31 | -0.15 | -0.16 | 5.0 | -26.4 | -3.50 | -10.70 | 0.48 | -2 | 19.86 | 3.60 | 0.33 | 0.41 | -0.35 | 38.9 | 5 | 1 | 2 | 3 | 2 | 5 | 8 | 7 | -0.135169763 | 0.376817502 | 32 |
| 2 | 8 | 1 | 53 | 1 | -0.36 | -0.45 | -1.20 | -40 | 0.39 | -0.11 | 5.0 | -5.9 | 1.70 | -3.00 | 0.05 | 18 | 19.26 | 4.60 | 0.08 | -0.13 | -0.16 | 24.0 | 7 | 1 | 4 | 5 | 5 | 4 | 3 | 7 | 0.162247681 | -0.019717871 | 23 |
# Tipos de varibales
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux
| Tipo de Variable | Numero_Columna | |
|---|---|---|
| <chr> | <int> | |
| STATUS | character | 1 |
| CYCLES_BETWEEN_PET1_PET2 | numeric | 2 |
| GENDER | character | 3 |
| AGE | numeric | 4 |
| TNM_STAGE | character | 5 |
| DIFF_WBC | numeric | 6 |
| DIFF_RBC | numeric | 7 |
| DIFF_HB | numeric | 8 |
| DIFF_PLT | numeric | 9 |
| DIFF_CRP | numeric | 10 |
| DIFF_ALBUMIN | numeric | 11 |
| DIFF_LDH | numeric | 12 |
| DIFF_eGFR | numeric | 13 |
| DIFF_AST | numeric | 14 |
| DIFF_ALT | numeric | 15 |
| DIFF_K | numeric | 16 |
| DIFF_BGL | numeric | 17 |
| BMI | numeric | 18 |
| DIFF_BW | numeric | 19 |
| DIFF_SPLEEN_UPTAKE | numeric | 20 |
| DIFF_BM_UPTAKE | numeric | 21 |
| DIFF_LIVER_UPTAKE | numeric | 22 |
| DIFF_ESTIMATED_SPLEEN_VOL | numeric | 23 |
| DIAGNOSTIC | character | 24 |
| TREATMENT | character | 25 |
| ECOGPS | character | 26 |
| COMORBIDITIES | character | 27 |
| CTCNCI | character | 28 |
| ACTION_TAKEN_ | character | 29 |
| TIME_BETWEE_PET | numeric | 30 |
| dias | numeric | 31 |
| DIFF_SLR | numeric | 32 |
| DIFF_BMLR | numeric | 33 |
| OVERALL_TIME | numeric | 34 |
overview(datos)
plot(overview(datos))
| division | metrics | value |
|---|---|---|
| <chr> | <chr> | <dbl> |
| size | observations | 59 |
| size | variables | 34 |
| size | values | 2006 |
| size | memory size | 23968 |
| duplicated | duplicate observation | 0 |
| missing | complete observation | 59 |
| missing | missing observation | 0 |
| missing | missing variables | 0 |
| missing | missing values | 0 |
| data type | numerics | 25 |
| data type | integers | 0 |
| data type | factors/ordered | 0 |
| data type | characters | 9 |
| data type | Dates | 0 |
| data type | POSIXcts | 0 |
| data type | others | 0 |
No quitamos ni outliers ni extremos ya que son pocas observaciones y ademas son mediciones reales
# pasar variables a factor
datos$STATUS <- as.factor(datos$STATUS)
datos$GENDER <- as.factor(datos$GENDER)
datos$COMORBIDITIES <- as.factor(datos$COMORBIDITIES)
datos$CTCNCI <- as.factor(datos$CTCNCI)
datos$ACTION_TAKEN_ <- as.factor(datos$ACTION_TAKEN_)
datos$TNM_STAGE <- as.factor(datos$TNM_STAGE)
datos$DIAGNOSTIC <- as.factor(datos$DIAGNOSTIC)
datos$TREATMENT <- as.factor(datos$TREATMENT)
datos$ECOGPS <- as.factor(datos$ECOGPS)
# vemos los cambios
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux
| Tipo de Variable | Numero_Columna | |
|---|---|---|
| <chr> | <int> | |
| STATUS | factor | 1 |
| CYCLES_BETWEEN_PET1_PET2 | numeric | 2 |
| GENDER | factor | 3 |
| AGE | numeric | 4 |
| TNM_STAGE | factor | 5 |
| DIFF_WBC | numeric | 6 |
| DIFF_RBC | numeric | 7 |
| DIFF_HB | numeric | 8 |
| DIFF_PLT | numeric | 9 |
| DIFF_CRP | numeric | 10 |
| DIFF_ALBUMIN | numeric | 11 |
| DIFF_LDH | numeric | 12 |
| DIFF_eGFR | numeric | 13 |
| DIFF_AST | numeric | 14 |
| DIFF_ALT | numeric | 15 |
| DIFF_K | numeric | 16 |
| DIFF_BGL | numeric | 17 |
| BMI | numeric | 18 |
| DIFF_BW | numeric | 19 |
| DIFF_SPLEEN_UPTAKE | numeric | 20 |
| DIFF_BM_UPTAKE | numeric | 21 |
| DIFF_LIVER_UPTAKE | numeric | 22 |
| DIFF_ESTIMATED_SPLEEN_VOL | numeric | 23 |
| DIAGNOSTIC | factor | 24 |
| TREATMENT | factor | 25 |
| ECOGPS | factor | 26 |
| COMORBIDITIES | factor | 27 |
| CTCNCI | factor | 28 |
| ACTION_TAKEN_ | factor | 29 |
| TIME_BETWEE_PET | numeric | 30 |
| dias | numeric | 31 |
| DIFF_SLR | numeric | 32 |
| DIFF_BMLR | numeric | 33 |
| OVERALL_TIME | numeric | 34 |
# Identificar variable respuesta
respuesta <- c("STATUS")
respuesta
# Identificar variables numericas de entrada
tipos_var <- t(t(sapply(datos, class)))
var_num <- colnames(datos)[tipos_var=="integer"|tipos_var=="numeric"]
var_num <- var_num[var_num!=respuesta]
as.data.frame(var_num)
| var_num |
|---|
| <chr> |
| CYCLES_BETWEEN_PET1_PET2 |
| AGE |
| DIFF_WBC |
| DIFF_RBC |
| DIFF_HB |
| DIFF_PLT |
| DIFF_CRP |
| DIFF_ALBUMIN |
| DIFF_LDH |
| DIFF_eGFR |
| DIFF_AST |
| DIFF_ALT |
| DIFF_K |
| DIFF_BGL |
| BMI |
| DIFF_BW |
| DIFF_SPLEEN_UPTAKE |
| DIFF_BM_UPTAKE |
| DIFF_LIVER_UPTAKE |
| DIFF_ESTIMATED_SPLEEN_VOL |
| TIME_BETWEE_PET |
| dias |
| DIFF_SLR |
| DIFF_BMLR |
| OVERALL_TIME |
# Identificar variables cualitativas
tipos_var <- t(t(sapply(datos, class)))
var_cual <- colnames(datos)[tipos_var=="character"|tipos_var=="factor"]
var_cual <- var_cual[var_cual!=respuesta]
as.data.frame(var_cual)
| var_cual |
|---|
| <chr> |
| GENDER |
| TNM_STAGE |
| DIAGNOSTIC |
| TREATMENT |
| ECOGPS |
| COMORBIDITIES |
| CTCNCI |
| ACTION_TAKEN_ |
# Tabla de frecuencias:
as.data.frame(table(datos$STATUS))
| Var1 | Freq |
|---|---|
| <fct> | <int> |
| ALIVE | 38 |
| DEATH | 21 |
# Barplot
barplot(table(datos$STATUS))
# Descripcion de variables cuantitativas:
d_uni<-dlookr::describe(datos)
d_uni
| described_variables | n | na | mean | sd | se_mean | IQR | skewness | kurtosis | p00 | p01 | p05 | p10 | p20 | p25 | p30 | p40 | p50 | p60 | p70 | p75 | p80 | p90 | p95 | p99 | p100 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <int> | <int> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| CYCLES_BETWEEN_PET1_PET2 | 59 | 0 | 7.06779661 | 7.4506755 | 0.96999533 | 4.5000000 | 3.5461362 | 16.079740747 | 2.0000000 | 2.0000000 | 2.0000000 | 2.0000000 | 3.0000000 | 3.0000000 | 3.40000000 | 4.00000000 | 5.000000000 | 5.80000000 | 6.00000000 | 7.5000000 | 9.4000000 | 14.0000000 | 18.2000000 | 36.9800000 | 48.0000000 |
| AGE | 59 | 0 | 69.38983051 | 8.6223786 | 1.12253807 | 12.5000000 | -0.1966440 | -0.589411859 | 52.0000000 | 52.5800000 | 54.0000000 | 56.0000000 | 61.0000000 | 63.5000000 | 65.00000000 | 68.00000000 | 70.000000000 | 72.80000000 | 74.60000000 | 76.0000000 | 76.4000000 | 79.0000000 | 83.0000000 | 86.4200000 | 87.0000000 |
| DIFF_WBC | 59 | 0 | -0.17525424 | 2.7263955 | 0.35494646 | 3.8000000 | 0.5057711 | 0.129045347 | -5.7000000 | -5.1664000 | -3.8570000 | -3.1240000 | -2.5500000 | -2.1800000 | -1.88400000 | -1.27000000 | -0.470000000 | 0.08000000 | 1.13000000 | 1.6200000 | 2.1120000 | 3.4840000 | 4.2590000 | 6.4292000 | 7.7400000 |
| DIFF_RBC | 59 | 0 | -0.31101695 | 0.7000066 | 0.09113310 | 0.8950000 | -0.1435844 | -0.229988897 | -1.9700000 | -1.9468000 | -1.3230000 | -1.2420000 | -0.8760000 | -0.7750000 | -0.66400000 | -0.43800000 | -0.290000000 | -0.13400000 | 0.06200000 | 0.1200000 | 0.1560000 | 0.6460000 | 0.8310000 | 0.9992000 | 1.1500000 |
| DIFF_HB | 59 | 0 | -0.09593220 | 2.1214367 | 0.27618753 | 2.8000000 | 0.3373839 | 0.254904377 | -5.2000000 | -4.2140000 | -3.2300000 | -2.6200000 | -1.7800000 | -1.5500000 | -1.16000000 | -0.80000000 | -0.400000000 | 0.36000000 | 0.82000000 | 1.2500000 | 1.6400000 | 2.4400000 | 3.7100000 | 4.7560000 | 5.8000000 |
| DIFF_PLT | 59 | 0 | -13.27118644 | 129.0645161 | 16.80276880 | 84.5000000 | 0.7237460 | 3.418977080 | -354.0000000 | -298.3200000 | -229.4000000 | -163.8000000 | -82.0000000 | -60.5000000 | -58.60000000 | -23.00000000 | -11.000000000 | 0.80000000 | 15.00000000 | 24.0000000 | 38.0000000 | 96.0000000 | 201.5000000 | 377.7400000 | 463.0000000 |
| DIFF_CRP | 59 | 0 | 0.33372881 | 4.4082997 | 0.57391174 | 0.5600000 | 0.9022780 | 8.168553321 | -15.8800000 | -10.8630000 | -4.1300000 | -2.6600000 | -0.5000000 | -0.2450000 | -0.10000000 | -0.01800000 | 0.020000000 | 0.04000000 | 0.23800000 | 0.3150000 | 0.4700000 | 3.3480000 | 7.2440000 | 16.3104000 | 17.8300000 |
| DIFF_ALBUMIN | 59 | 0 | -0.05762712 | 0.7953146 | 0.10354114 | 0.3900000 | 0.6459202 | 3.439907176 | -2.2800000 | -1.9436000 | -1.3650000 | -0.7560000 | -0.4480000 | -0.2800000 | -0.21000000 | -0.12800000 | -0.100000000 | -0.05200000 | 0.05000000 | 0.1100000 | 0.1880000 | 1.0140000 | 1.3540000 | 2.1978000 | 2.8300000 |
| DIFF_LDH | 59 | 0 | -2.79322034 | 125.8579055 | 16.38530365 | 47.0000000 | -0.8048823 | 7.846212457 | -510.0000000 | -407.3400000 | -273.3000000 | -69.0000000 | -25.0000000 | -14.0000000 | -8.60000000 | -1.60000000 | 5.000000000 | 15.80000000 | 28.00000000 | 33.0000000 | 42.6000000 | 72.4000000 | 109.7000000 | 310.7600000 | 472.0000000 |
| DIFF_eGFR | 59 | 0 | -2.49830508 | 16.4224247 | 2.13801758 | 15.9500000 | 0.6730682 | 1.790030255 | -39.2000000 | -35.4300000 | -26.7700000 | -24.1800000 | -13.1400000 | -10.3000000 | -9.32000000 | -5.86000000 | -2.800000000 | -0.34000000 | 3.18000000 | 5.6500000 | 8.0600000 | 13.5400000 | 24.7600000 | 46.3080000 | 47.7000000 |
| DIFF_AST | 59 | 0 | -7.27813559 | 33.1784402 | 4.31946500 | 9.4500000 | -5.2484541 | 30.880490044 | -219.6000000 | -156.9020000 | -23.3200000 | -12.3200000 | -6.7800000 | -5.9500000 | -5.18000000 | -2.90000000 | -0.700000000 | 0.00000000 | 1.50000000 | 3.5000000 | 4.5200000 | 7.3400000 | 12.0300000 | 16.9860000 | 18.9000000 |
| DIFF_ALT | 59 | 0 | -9.48949153 | 36.3504299 | 4.73242289 | 9.9500000 | -5.4936828 | 34.708950221 | -250.8000000 | -154.5780000 | -29.9800000 | -17.1800000 | -10.0200000 | -9.0000000 | -8.16000000 | -5.36000000 | -2.400000000 | -0.80000000 | 0.52000000 | 0.9500000 | 2.4400000 | 5.7400000 | 14.5500000 | 18.7620000 | 19.4000000 |
| DIFF_K | 59 | 0 | 0.14796610 | 0.4109064 | 0.05349546 | 0.5450000 | -0.1046069 | -0.003291498 | -0.9000000 | -0.7666000 | -0.5680000 | -0.3320000 | -0.1780000 | -0.0850000 | -0.01600000 | 0.05000000 | 0.100000000 | 0.22200000 | 0.35600000 | 0.4600000 | 0.4920000 | 0.6900000 | 0.7720000 | 0.9772000 | 1.0700000 |
| DIFF_BGL | 59 | 0 | -1.27118644 | 37.1413101 | 4.83538671 | 28.0000000 | -1.8484106 | 6.368310476 | -160.0000000 | -135.6400000 | -70.0000000 | -29.8000000 | -13.0000000 | -11.5000000 | -7.60000000 | -1.80000000 | 2.000000000 | 10.60000000 | 14.60000000 | 16.5000000 | 18.4000000 | 29.0000000 | 45.1000000 | 66.5200000 | 70.0000000 |
| BMI | 59 | 0 | 21.54457627 | 4.0359947 | 0.52544175 | 5.5200000 | 0.7826975 | 1.399355371 | 14.0800000 | 14.8630000 | 16.2330000 | 16.6460000 | 17.9740000 | 18.2350000 | 19.14600000 | 20.17800000 | 21.340000000 | 22.64600000 | 23.36200000 | 23.7550000 | 24.4300000 | 26.1180000 | 27.3730000 | 32.1850000 | 35.8100000 |
| DIFF_BW | 59 | 0 | 3.00033898 | 9.1860843 | 1.19592631 | 7.8500000 | 2.0129510 | 6.438370916 | -11.1000000 | -10.9260000 | -6.7500000 | -5.4400000 | -2.4000000 | -2.2000000 | -1.30000000 | 0.00000000 | 0.300000000 | 3.60000000 | 5.10000000 | 5.6500000 | 6.7200000 | 12.7800000 | 19.2400000 | 33.6844000 | 44.0200000 |
| DIFF_SPLEEN_UPTAKE | 59 | 0 | 0.05067797 | 0.3633743 | 0.04730731 | 0.3650000 | 0.4149319 | 1.664027013 | -0.8000000 | -0.7188000 | -0.5580000 | -0.4020000 | -0.1600000 | -0.1250000 | -0.07600000 | -0.04000000 | 0.050000000 | 0.11600000 | 0.21200000 | 0.2400000 | 0.2820000 | 0.3520000 | 0.6150000 | 1.1300000 | 1.1300000 |
| DIFF_BM_UPTAKE | 59 | 0 | 0.10711864 | 0.6017591 | 0.07834236 | 0.7100000 | 0.6111772 | 1.721229694 | -1.2000000 | -1.0898000 | -0.8020000 | -0.6240000 | -0.3760000 | -0.2600000 | -0.19600000 | -0.00800000 | 0.110000000 | 0.21000000 | 0.40200000 | 0.4500000 | 0.5200000 | 0.7080000 | 0.8460000 | 1.9350000 | 2.0800000 |
| DIFF_LIVER_UPTAKE | 59 | 0 | 0.01779661 | 0.5004070 | 0.06514744 | 0.7450000 | -0.5918683 | 0.488986741 | -1.5900000 | -1.1724000 | -0.8410000 | -0.6600000 | -0.3440000 | -0.2950000 | -0.24600000 | -0.09600000 | 0.020000000 | 0.18800000 | 0.32800000 | 0.4500000 | 0.5040000 | 0.6020000 | 0.6620000 | 0.8568000 | 0.8800000 |
| DIFF_ESTIMATED_SPLEEN_VOL | 59 | 0 | 8.61694915 | 50.0321902 | 6.51363635 | 40.1500000 | 0.1688579 | 1.731668976 | -110.2000000 | -97.0920000 | -85.0500000 | -58.1800000 | -18.9400000 | -6.6000000 | -2.28000000 | 2.62000000 | 9.100000000 | 18.90000000 | 30.56000000 | 33.5500000 | 36.2000000 | 58.0800000 | 85.6600000 | 131.9920000 | 177.0000000 |
| TIME_BETWEE_PET | 59 | 0 | 9.62711864 | 7.6404060 | 0.99469613 | 8.0000000 | 1.6870456 | 3.317725303 | 1.0000000 | 1.0000000 | 2.0000000 | 2.8000000 | 4.0000000 | 4.0000000 | 5.00000000 | 6.00000000 | 8.000000000 | 9.00000000 | 10.60000000 | 12.0000000 | 13.4000000 | 20.0000000 | 25.0000000 | 35.1000000 | 38.0000000 |
| dias | 59 | 0 | 57.15254237 | 133.9282893 | 17.43597813 | 34.0000000 | 3.2949717 | 12.075154478 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.00000000 | 0.00000000 | 0.000000000 | 7.00000000 | 26.80000000 | 34.0000000 | 50.4000000 | 202.6000000 | 363.1000000 | 564.4800000 | 735.0000000 |
| DIFF_SLR | 59 | 0 | 0.00266275 | 0.2193508 | 0.02855705 | 0.2609162 | 0.3392356 | 1.204043701 | -0.5135982 | -0.5072080 | -0.3150038 | -0.2470626 | -0.1597951 | -0.1359963 | -0.11124706 | -0.04521368 | -0.002886671 | 0.04814346 | 0.09843415 | 0.1249199 | 0.1628149 | 0.2300977 | 0.3227746 | 0.5845516 | 0.6887497 |
| DIFF_BMLR | 59 | 0 | 0.02945905 | 0.3024081 | 0.03937018 | 0.3195083 | 0.3246088 | 1.244142990 | -0.6207071 | -0.6198529 | -0.5169781 | -0.3230395 | -0.1749106 | -0.1383526 | -0.09581267 | -0.04493716 | 0.034875129 | 0.08355615 | 0.14641308 | 0.1811556 | 0.2443964 | 0.3808468 | 0.4981993 | 0.7878837 | 1.0200322 |
| OVERALL_TIME | 59 | 0 | 19.10169492 | 15.8415592 | 2.06239534 | 15.0000000 | 1.3537447 | 1.563910333 | 1.0000000 | 1.0000000 | 1.9000000 | 3.0000000 | 6.0000000 | 8.5000000 | 10.40000000 | 12.20000000 | 15.000000000 | 17.80000000 | 21.60000000 | 23.5000000 | 29.4000000 | 41.2000000 | 51.3000000 | 64.4200000 | 65.0000000 |
for (i in 1:length(var_num)) {
name_i <- as.name(var_num[i])
r <- list()
# Histograma de densidad
r[[1]]<-ggplot(datos, aes(x=!!name_i)) +
geom_density(fill="#69b3a2", color="black", alpha=0.6)+
theme_light() +
#theme(legend.position = "none") +
xlab("") +
ylab("Densidad de Frecuencia")
# Boxplot
r[[2]]<-ggplot(data=datos, aes(y=!!name_i)) +
geom_boxplot(size = 0.4) +
theme_light() +
theme(legend.position = "none")+
xlab("")
# Qqplot
r[[3]]<-ggqqplot(datos, x = var_num[i],color = "#FF6666",add.params = list(color = "black"))+
xlab("") + ylab("Cuartiles reales") +
theme_minimal() +
#ggtitle("qqplot") +
theme(plot.title = element_text(hjust = 0.5))
grid.arrange(r[[1]], r[[2]], r[[3]],
nrow=1, ncol = 3,
top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}
# Analisis de outliers:
diag_out <- diagnose_outlier(datos)
diag_out
| variables | outliers_cnt | outliers_ratio | outliers_mean | with_mean | without_mean |
|---|---|---|---|---|---|
| <chr> | <int> | <dbl> | <dbl> | <dbl> | <dbl> |
| CYCLES_BETWEEN_PET1_PET2 | 5 | 8.474576 | 26.00000000 | 7.06779661 | 5.314814815 |
| AGE | 0 | 0.000000 | NaN | 69.38983051 | 69.389830508 |
| DIFF_WBC | 1 | 1.694915 | 7.74000000 | -0.17525424 | -0.311724138 |
| DIFF_RBC | 0 | 0.000000 | NaN | -0.31101695 | -0.311016949 |
| DIFF_HB | 1 | 1.694915 | 5.80000000 | -0.09593220 | -0.197586207 |
| DIFF_PLT | 10 | 16.949153 | 12.50000000 | -13.27118644 | -18.530612245 |
| DIFF_CRP | 21 | 35.593220 | 0.84142857 | 0.33372881 | 0.053157895 |
| DIFF_ALBUMIN | 13 | 22.033898 | 0.14076923 | -0.05762712 | -0.113695652 |
| DIFF_LDH | 8 | 13.559322 | -89.00000000 | -2.79322034 | 10.729411765 |
| DIFF_eGFR | 4 | 6.779661 | 22.02500000 | -2.49830508 | -4.281818182 |
| DIFF_AST | 4 | 6.779661 | -92.02500000 | -7.27813559 | -1.114727273 |
| DIFF_ALT | 6 | 10.169492 | -60.98333333 | -9.48949153 | -3.660000000 |
| DIFF_K | 0 | 0.000000 | NaN | 0.14796610 | 0.147966102 |
| DIFF_BGL | 8 | 13.559322 | -34.50000000 | -1.27118644 | 3.941176471 |
| BMI | 1 | 1.694915 | 35.81000000 | 21.54457627 | 21.298620690 |
| DIFF_BW | 4 | 6.779661 | 28.25500000 | 3.00033898 | 1.163636364 |
| DIFF_SPLEEN_UPTAKE | 3 | 5.084746 | 0.48666667 | 0.05067797 | 0.027321429 |
| DIFF_BM_UPTAKE | 2 | 3.389831 | 1.95500000 | 0.10711864 | 0.042280702 |
| DIFF_LIVER_UPTAKE | 1 | 1.694915 | -1.59000000 | 0.01779661 | 0.045517241 |
| DIFF_ESTIMATED_SPLEEN_VOL | 9 | 15.254237 | -15.83333333 | 8.61694915 | 13.018000000 |
| TIME_BETWEE_PET | 4 | 6.779661 | 30.25000000 | 9.62711864 | 8.127272727 |
| dias | 10 | 16.949153 | 293.80000000 | 57.15254237 | 8.857142857 |
| DIFF_SLR | 1 | 1.694915 | 0.68874973 | 0.00266275 | -0.009166336 |
| DIFF_BMLR | 3 | 5.084746 | -0.07330307 | 0.02945905 | 0.034964164 |
| OVERALL_TIME | 5 | 8.474576 | 57.80000000 | 19.10169492 | 15.518518519 |
# Plot analisis de outliers:
plot_outlier(datos)
# Plot de normalidad:
plot_normality(datos)
vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
'AGE',
'DIFF_WBC',
'DIFF_RBC',
'DIFF_HB',
'DIFF_PLT',
'DIFF_CRP',
'DIFF_ALBUMIN',
'DIFF_LDH',
'DIFF_eGFR',
'DIFF_AST',
'DIFF_ALT',
'DIFF_K',
'DIFF_BGL',
'BMI',
'DIFF_BW',
'DIFF_SPLEEN_UPTAKE',
'DIFF_BM_UPTAKE',
'DIFF_LIVER_UPTAKE',
'DIFF_ESTIMATED_SPLEEN_VOL',
'TIME_BETWEE_PET',
'dias',
'DIFF_SLR',
'DIFF_BMLR',
'OVERALL_TIME')
options(repr.plot.width=16, repr.plot.height=10)
plot_correlate(datos[,vec_var_num],method = "spearman")
# coeficiente de correlación
library(Hmisc)
vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
'AGE',
'DIFF_WBC',
'DIFF_RBC',
'DIFF_HB',
'DIFF_PLT',
'DIFF_CRP',
'DIFF_ALBUMIN',
'DIFF_LDH',
'DIFF_eGFR',
'DIFF_AST',
'DIFF_ALT',
'DIFF_K',
'DIFF_BGL',
'BMI',
'DIFF_BW',
'DIFF_SPLEEN_UPTAKE',
'DIFF_BM_UPTAKE',
'DIFF_LIVER_UPTAKE',
'DIFF_ESTIMATED_SPLEEN_VOL',
'TIME_BETWEE_PET',
'dias',
'DIFF_SLR',
'DIFF_BMLR',
'OVERALL_TIME')
# Correlaciones por parejas:
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
# Calculamos la matriz de correlacion y visualizamos la correlacion de mayor intensidad
tcor<-rcorr(as.matrix(datos[,vec_var_num]),type = "spearman")
corr_data <- flattenCorrMatrix(tcor$r, tcor$P) %>%
arrange(desc(abs(cor))) #%>%
corr_data
options(warn = -1)
Loading required package: survival
Attaching package: 'survival'
The following object is masked from 'package:caret':
cluster
Loading required package: Formula
Attaching package: 'Hmisc'
The following object is masked from 'package:dlookr':
describe
The following objects are masked from 'package:dplyr':
src, summarize
The following object is masked from 'package:plotly':
subplot
The following objects are masked from 'package:base':
format.pval, units
| row | column | cor | p |
|---|---|---|---|
| <chr> | <chr> | <dbl> | <dbl> |
| DIFF_AST | DIFF_ALT | 0.850032878 | 0.000000e+00 |
| DIFF_RBC | DIFF_HB | 0.651414630 | 2.309223e-08 |
| DIFF_SLR | DIFF_BMLR | 0.587655981 | 9.917998e-07 |
| BMI | DIFF_BW | -0.491864226 | 7.608448e-05 |
| DIFF_RBC | TIME_BETWEE_PET | 0.456778390 | 2.763295e-04 |
| CYCLES_BETWEEN_PET1_PET2 | TIME_BETWEE_PET | 0.405997284 | 1.420657e-03 |
| DIFF_LDH | DIFF_AST | 0.403741050 | 1.519114e-03 |
| DIFF_LDH | DIFF_ALT | 0.391021614 | 2.197503e-03 |
| DIFF_SPLEEN_UPTAKE | DIFF_BMLR | 0.361416383 | 4.916694e-03 |
| DIFF_PLT | DIFF_ALBUMIN | -0.344376087 | 7.566172e-03 |
| DIFF_WBC | DIFF_LIVER_UPTAKE | -0.341969139 | 8.026443e-03 |
| DIFF_WBC | DIFF_RBC | 0.334297738 | 9.660078e-03 |
| DIFF_PLT | DIFF_LIVER_UPTAKE | -0.332169161 | 1.016160e-02 |
| DIFF_CRP | DIFF_BGL | 0.332110388 | 1.017576e-02 |
| DIFF_HB | DIFF_BGL | -0.324202650 | 1.224414e-02 |
| DIFF_CRP | DIFF_ALBUMIN | -0.312756662 | 1.587521e-02 |
| DIFF_ESTIMATED_SPLEEN_VOL | DIFF_BMLR | -0.312219641 | 1.606611e-02 |
| DIFF_WBC | TIME_BETWEE_PET | 0.309196172 | 1.717791e-02 |
| DIFF_HB | BMI | -0.308860281 | 1.730539e-02 |
| DIFF_WBC | DIFF_PLT | 0.306742460 | 1.812788e-02 |
| DIFF_RBC | BMI | -0.301445125 | 2.033234e-02 |
| BMI | DIFF_SPLEEN_UPTAKE | 0.298415856 | 2.169234e-02 |
| DIFF_K | DIFF_ESTIMATED_SPLEEN_VOL | -0.295751805 | 2.295131e-02 |
| DIFF_RBC | DIFF_BGL | -0.285039905 | 2.865490e-02 |
| DIFF_PLT | DIFF_eGFR | 0.267531747 | 4.051234e-02 |
| DIFF_RBC | DIFF_eGFR | -0.265528881 | 4.209569e-02 |
| DIFF_ALBUMIN | DIFF_ESTIMATED_SPLEEN_VOL | -0.263249412 | 4.395935e-02 |
| TIME_BETWEE_PET | OVERALL_TIME | -0.251015351 | 5.515436e-02 |
| DIFF_K | BMI | -0.245995794 | 6.037113e-02 |
| AGE | DIFF_K | 0.243269865 | 6.336676e-02 |
| DIFF_WBC | DIFF_CRP | 0.240106391 | 6.699187e-02 |
| DIFF_eGFR | DIFF_LIVER_UPTAKE | -0.239636454 | 6.754429e-02 |
| CYCLES_BETWEEN_PET1_PET2 | dias | 0.232476215 | 7.642104e-02 |
| DIFF_HB | DIFF_SLR | -0.232138423 | 7.686165e-02 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_RBC | 0.231469664 | 7.773990e-02 |
| BMI | OVERALL_TIME | -0.229712003 | 8.008611e-02 |
| DIFF_eGFR | DIFF_ESTIMATED_SPLEEN_VOL | 0.225456246 | 8.599905e-02 |
| DIFF_PLT | DIFF_BW | 0.224662286 | 8.713923e-02 |
| DIFF_PLT | DIFF_BM_UPTAKE | 0.218676019 | 9.612251e-02 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_SPLEEN_UPTAKE | 0.218178907 | 9.689979e-02 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_BW | 0.217188918 | 9.846228e-02 |
| DIFF_eGFR | DIFF_K | -0.215671949 | 1.008944e-01 |
| DIFF_CRP | DIFF_SLR | 0.213549813 | 1.043747e-01 |
| DIFF_SPLEEN_UPTAKE | DIFF_SLR | 0.212471505 | 1.061782e-01 |
| AGE | DIFF_ESTIMATED_SPLEEN_VOL | -0.212144856 | 1.067293e-01 |
| DIFF_ALBUMIN | DIFF_SPLEEN_UPTAKE | -0.210972439 | 1.087254e-01 |
| AGE | DIFF_BM_UPTAKE | -0.209992087 | 1.104165e-01 |
| DIFF_ALBUMIN | DIFF_BW | -0.208511848 | 1.130081e-01 |
| DIFF_PLT | DIFF_ESTIMATED_SPLEEN_VOL | 0.208346729 | 1.133001e-01 |
| DIFF_ESTIMATED_SPLEEN_VOL | dias | 0.202249755 | 1.244907e-01 |
| DIFF_BW | DIFF_SLR | -0.201912282 | 1.251337e-01 |
| DIFF_LDH | dias | -0.201125812 | 1.266422e-01 |
| DIFF_LDH | DIFF_SPLEEN_UPTAKE | -0.199789542 | 1.292366e-01 |
| DIFF_BW | DIFF_BM_UPTAKE | -0.199406440 | 1.299878e-01 |
| DIFF_BW | OVERALL_TIME | 0.198338042 | 1.321001e-01 |
| DIFF_CRP | DIFF_LIVER_UPTAKE | -0.197112295 | 1.345553e-01 |
| DIFF_CRP | DIFF_SPLEEN_UPTAKE | 0.195588945 | 1.376543e-01 |
| DIFF_eGFR | DIFF_BMLR | -0.195067068 | 1.387282e-01 |
| DIFF_CRP | DIFF_AST | -0.192540957 | 1.440151e-01 |
| DIFF_SPLEEN_UPTAKE | DIFF_LIVER_UPTAKE | 0.191941779 | 1.452910e-01 |
| DIFF_AST | DIFF_LIVER_UPTAKE | 0.190867246 | 1.476000e-01 |
| DIFF_eGFR | dias | -0.187906878 | 1.541026e-01 |
| DIFF_HB | DIFF_LDH | 0.187568508 | 1.548592e-01 |
| DIFF_BGL | DIFF_SPLEEN_UPTAKE | 0.186031895 | 1.583293e-01 |
| DIFF_RBC | DIFF_SLR | -0.184169382 | 1.626119e-01 |
| DIFF_ALT | DIFF_LIVER_UPTAKE | 0.183821381 | 1.634214e-01 |
| DIFF_LIVER_UPTAKE | DIFF_ESTIMATED_SPLEEN_VOL | -0.182500950 | 1.665199e-01 |
| DIFF_K | DIFF_BW | 0.182231594 | 1.671572e-01 |
| DIFF_HB | DIFF_CRP | -0.181204326 | 1.696041e-01 |
| DIFF_ESTIMATED_SPLEEN_VOL | OVERALL_TIME | 0.179380374 | 1.740129e-01 |
| AGE | dias | -0.176621115 | 1.808401e-01 |
| DIFF_LIVER_UPTAKE | OVERALL_TIME | -0.176475791 | 1.812049e-01 |
| DIFF_SPLEEN_UPTAKE | DIFF_BM_UPTAKE | 0.175052979 | 1.848053e-01 |
| DIFF_LDH | DIFF_ESTIMATED_SPLEEN_VOL | 0.174097293 | 1.872523e-01 |
| DIFF_BW | DIFF_LIVER_UPTAKE | -0.172660843 | 1.909740e-01 |
| AGE | DIFF_BGL | -0.171956240 | 1.928189e-01 |
| BMI | DIFF_SLR | 0.169934832 | 1.981820e-01 |
| DIFF_AST | DIFF_SLR | -0.167937927 | 2.035837e-01 |
| DIFF_WBC | DIFF_K | -0.165680215 | 2.098155e-01 |
| DIFF_ALBUMIN | DIFF_LDH | -0.164825369 | 2.122098e-01 |
| DIFF_ALBUMIN | DIFF_BM_UPTAKE | -0.162582380 | 2.185832e-01 |
| DIFF_CRP | DIFF_ALT | -0.161585902 | 2.214572e-01 |
| DIFF_BGL | DIFF_BM_UPTAKE | 0.158184744 | 2.314647e-01 |
| AGE | DIFF_BMLR | 0.157801682 | 2.326111e-01 |
| DIFF_HB | TIME_BETWEE_PET | 0.156549779 | 2.363851e-01 |
| DIFF_SPLEEN_UPTAKE | dias | 0.154408799 | 2.429364e-01 |
| AGE | DIFF_SPLEEN_UPTAKE | 0.154006050 | 2.441826e-01 |
| DIFF_HB | DIFF_BMLR | -0.152201553 | 2.498195e-01 |
| DIFF_LDH | DIFF_BMLR | -0.151946458 | 2.506234e-01 |
| DIFF_ALT | DIFF_ESTIMATED_SPLEEN_VOL | -0.151494784 | 2.520512e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_AST | 0.149772343 | 2.575466e-01 |
| DIFF_BMLR | OVERALL_TIME | 0.148252705 | 2.624617e-01 |
| DIFF_BM_UPTAKE | DIFF_BMLR | 0.148199021 | 2.626365e-01 |
| DIFF_BGL | DIFF_SLR | 0.146763519 | 2.673391e-01 |
| DIFF_RBC | DIFF_PLT | 0.145823592 | 2.704486e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_HB | 0.145484862 | 2.715751e-01 |
| DIFF_HB | DIFF_BW | 0.144768406 | 2.739680e-01 |
| DIFF_ALT | DIFF_SPLEEN_UPTAKE | -0.141742296 | 2.842295e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_WBC | 0.141695974 | 2.843886e-01 |
| DIFF_K | DIFF_BMLR | 0.141082599 | 2.864997e-01 |
| DIFF_AST | DIFF_BW | 0.140893891 | 2.871513e-01 |
| DIFF_BM_UPTAKE | DIFF_ESTIMATED_SPLEEN_VOL | 0.138932726 | 2.939806e-01 |
| AGE | DIFF_eGFR | -0.137964927 | 2.973895e-01 |
| DIFF_ALT | DIFF_BGL | 0.137455792 | 2.991930e-01 |
| DIFF_RBC | DIFF_ALBUMIN | 0.137195879 | 3.001165e-01 |
| DIFF_BM_UPTAKE | DIFF_LIVER_UPTAKE | 0.135970424 | 3.044954e-01 |
| DIFF_LDH | DIFF_BM_UPTAKE | 0.134972015 | 3.080934e-01 |
| DIFF_PLT | OVERALL_TIME | 0.133347983 | 3.140042e-01 |
| DIFF_WBC | DIFF_AST | -0.133235917 | 3.144148e-01 |
| DIFF_PLT | DIFF_BGL | 0.132830543 | 3.159027e-01 |
| DIFF_HB | dias | -0.132626234 | 3.166543e-01 |
| DIFF_BW | TIME_BETWEE_PET | -0.132082056 | 3.186617e-01 |
| DIFF_AST | DIFF_BMLR | -0.131378222 | 3.212701e-01 |
| AGE | DIFF_BW | 0.129681968 | 3.276121e-01 |
| BMI | DIFF_BM_UPTAKE | 0.128559948 | 3.318502e-01 |
| DIFF_eGFR | DIFF_SPLEEN_UPTAKE | -0.128310050 | 3.327988e-01 |
| DIFF_PLT | DIFF_LDH | 0.125389101 | 3.440128e-01 |
| DIFF_ALT | BMI | 0.124347940 | 3.480660e-01 |
| DIFF_ALT | DIFF_SLR | -0.122711399 | 3.544966e-01 |
| DIFF_ALT | dias | -0.120804643 | 3.620803e-01 |
| CYCLES_BETWEEN_PET1_PET2 | BMI | -0.120506857 | 3.632736e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_LIVER_UPTAKE | 0.118003853 | 3.733978e-01 |
| TIME_BETWEE_PET | dias | 0.117319793 | 3.761940e-01 |
| DIFF_WBC | DIFF_eGFR | 0.116837383 | 3.781735e-01 |
| DIFF_ALBUMIN | DIFF_BGL | -0.116747784 | 3.785418e-01 |
| BMI | DIFF_BMLR | 0.116075864 | 3.813109e-01 |
| DIFF_BGL | DIFF_BMLR | -0.115896704 | 3.820512e-01 |
| DIFF_WBC | dias | -0.115709055 | 3.828276e-01 |
| TIME_BETWEE_PET | DIFF_SLR | -0.115694215 | 3.828891e-01 |
| AGE | DIFF_RBC | 0.114194672 | 3.891276e-01 |
| DIFF_eGFR | DIFF_BGL | 0.114025988 | 3.898332e-01 |
| DIFF_K | DIFF_BM_UPTAKE | 0.111859701 | 3.989608e-01 |
| BMI | DIFF_LIVER_UPTAKE | 0.111109488 | 4.021508e-01 |
| DIFF_RBC | dias | 0.110064711 | 4.066180e-01 |
| DIFF_HB | DIFF_LIVER_UPTAKE | 0.109662712 | 4.083445e-01 |
| DIFF_RBC | OVERALL_TIME | 0.109593674 | 4.086415e-01 |
| DIFF_RBC | DIFF_ESTIMATED_SPLEEN_VOL | -0.109325229 | 4.097972e-01 |
| DIFF_BW | DIFF_BMLR | -0.108083231 | 4.151691e-01 |
| DIFF_HB | DIFF_PLT | 0.108061499 | 4.152635e-01 |
| DIFF_CRP | OVERALL_TIME | 0.106974177 | 4.200000e-01 |
| DIFF_eGFR | DIFF_ALT | 0.106316758 | 4.228788e-01 |
| DIFF_RBC | DIFF_BW | 0.105614050 | 4.259682e-01 |
| DIFF_LDH | DIFF_K | 0.105609307 | 4.259891e-01 |
| DIFF_CRP | DIFF_BMLR | 0.103917930 | 4.334778e-01 |
| DIFF_ALBUMIN | DIFF_eGFR | -0.100619557 | 4.482927e-01 |
| DIFF_PLT | DIFF_CRP | 0.100242605 | 4.500035e-01 |
| dias | DIFF_BMLR | 0.098758901 | 4.567719e-01 |
| DIFF_ALBUMIN | DIFF_ALT | 0.098049244 | 4.600288e-01 |
| dias | DIFF_SLR | 0.096313054 | 4.680500e-01 |
| AGE | DIFF_PLT | 0.095504278 | 4.718122e-01 |
| DIFF_CRP | dias | 0.093189609 | 4.826683e-01 |
| DIFF_HB | DIFF_ALBUMIN | 0.092521958 | 4.858240e-01 |
| DIFF_WBC | DIFF_BMLR | -0.092347707 | 4.866494e-01 |
| BMI | dias | 0.091468396 | 4.908258e-01 |
| AGE | DIFF_ALT | -0.091036824 | 4.928824e-01 |
| DIFF_CRP | DIFF_BW | 0.090770858 | 4.941521e-01 |
| DIFF_eGFR | OVERALL_TIME | -0.090685795 | 4.945586e-01 |
| DIFF_LDH | DIFF_LIVER_UPTAKE | 0.089550058 | 5.000018e-01 |
| DIFF_ALT | DIFF_BMLR | -0.089542207 | 5.000396e-01 |
| TIME_BETWEE_PET | DIFF_BMLR | -0.089388757 | 5.007774e-01 |
| DIFF_BM_UPTAKE | OVERALL_TIME | 0.089095900 | 5.021871e-01 |
| DIFF_AST | DIFF_BM_UPTAKE | 0.087808513 | 5.084083e-01 |
| DIFF_PLT | DIFF_SPLEEN_UPTAKE | 0.086549845 | 5.145285e-01 |
| CYCLES_BETWEEN_PET1_PET2 | AGE | -0.086042255 | 5.170072e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_LDH | 0.085330284 | 5.204939e-01 |
| DIFF_WBC | DIFF_ESTIMATED_SPLEEN_VOL | 0.083082498 | 5.315790e-01 |
| DIFF_LDH | OVERALL_TIME | 0.082870278 | 5.326315e-01 |
| DIFF_eGFR | DIFF_BW | 0.082630383 | 5.338226e-01 |
| DIFF_SPLEEN_UPTAKE | OVERALL_TIME | 0.081705190 | 5.384283e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_ALT | 0.081684775 | 5.385302e-01 |
| DIFF_BW | DIFF_ESTIMATED_SPLEEN_VOL | 0.080582452 | 5.440434e-01 |
| DIFF_AST | DIFF_ESTIMATED_SPLEEN_VOL | -0.080541228 | 5.442501e-01 |
| DIFF_HB | DIFF_eGFR | -0.079834574 | 5.477993e-01 |
| DIFF_WBC | DIFF_ALT | -0.079767958 | 5.481345e-01 |
| DIFF_CRP | DIFF_K | -0.077960800 | 5.572641e-01 |
| DIFF_AST | DIFF_BGL | 0.077169292 | 5.612853e-01 |
| DIFF_ALBUMIN | DIFF_LIVER_UPTAKE | 0.076466720 | 5.648661e-01 |
| DIFF_ALBUMIN | DIFF_SLR | -0.075968788 | 5.674104e-01 |
| DIFF_BM_UPTAKE | dias | 0.075589384 | 5.693526e-01 |
| BMI | DIFF_ESTIMATED_SPLEEN_VOL | 0.074022122 | 5.774081e-01 |
| DIFF_AST | OVERALL_TIME | 0.072922167 | 5.830927e-01 |
| DIFF_PLT | BMI | -0.070594596 | 5.952046e-01 |
| DIFF_HB | DIFF_K | 0.070360572 | 5.964286e-01 |
| DIFF_K | DIFF_SLR | -0.069795991 | 5.993859e-01 |
| DIFF_K | TIME_BETWEE_PET | -0.069787035 | 5.994329e-01 |
| DIFF_ESTIMATED_SPLEEN_VOL | DIFF_SLR | -0.069375648 | 6.015919e-01 |
| AGE | DIFF_HB | -0.068925698 | 6.039573e-01 |
| AGE | DIFF_CRP | -0.068398989 | 6.067312e-01 |
| DIFF_BGL | OVERALL_TIME | -0.067583189 | 6.110387e-01 |
| DIFF_LDH | DIFF_BW | 0.067387974 | 6.120714e-01 |
| DIFF_PLT | DIFF_AST | 0.066987184 | 6.141939e-01 |
| DIFF_LIVER_UPTAKE | DIFF_SLR | 0.065637009 | 6.213673e-01 |
| DIFF_AST | DIFF_SPLEEN_UPTAKE | -0.065194517 | 6.237260e-01 |
| DIFF_eGFR | TIME_BETWEE_PET | -0.063390880 | 6.333787e-01 |
| DIFF_ALBUMIN | TIME_BETWEE_PET | 0.063158379 | 6.346275e-01 |
| DIFF_CRP | DIFF_ESTIMATED_SPLEEN_VOL | 0.062984072 | 6.355644e-01 |
| DIFF_K | OVERALL_TIME | -0.062138062 | 6.401196e-01 |
| DIFF_SPLEEN_UPTAKE | TIME_BETWEE_PET | -0.061730178 | 6.423206e-01 |
| DIFF_HB | DIFF_SPLEEN_UPTAKE | -0.061064909 | 6.459169e-01 |
| DIFF_RBC | DIFF_BM_UPTAKE | -0.060671596 | 6.480469e-01 |
| DIFF_WBC | DIFF_HB | 0.060633075 | 6.482557e-01 |
| DIFF_WBC | BMI | 0.060230577 | 6.504386e-01 |
| DIFF_WBC | DIFF_ALBUMIN | -0.059910864 | 6.521746e-01 |
| DIFF_RBC | DIFF_LIVER_UPTAKE | -0.059690806 | 6.533706e-01 |
| DIFF_eGFR | DIFF_AST | 0.057542814 | 6.650898e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_BM_UPTAKE | -0.057346038 | 6.661674e-01 |
| DIFF_PLT | DIFF_K | 0.055419961 | 6.767505e-01 |
| DIFF_RBC | DIFF_LDH | 0.054419781 | 6.822710e-01 |
| DIFF_ALT | DIFF_BM_UPTAKE | 0.053657539 | 6.864893e-01 |
| DIFF_RBC | DIFF_BMLR | -0.052749244 | 6.915283e-01 |
| DIFF_PLT | DIFF_SLR | 0.052109363 | 6.950862e-01 |
| DIFF_BW | dias | -0.051296450 | 6.996157e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_BGL | 0.050768693 | 7.025619e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_CRP | 0.050734770 | 7.027514e-01 |
| DIFF_WBC | DIFF_BW | -0.050526323 | 7.039164e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_ALBUMIN | -0.049830798 | 7.078084e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_K | -0.047788342 | 7.192803e-01 |
| DIFF_HB | DIFF_ALT | 0.047787456 | 7.192853e-01 |
| DIFF_WBC | DIFF_SLR | 0.047401260 | 7.214615e-01 |
| DIFF_RBC | DIFF_AST | 0.046379776 | 7.272280e-01 |
| DIFF_BGL | DIFF_LIVER_UPTAKE | 0.045701676 | 7.310644e-01 |
| DIFF_PLT | dias | 0.045659141 | 7.313053e-01 |
| DIFF_LDH | DIFF_eGFR | 0.043444587 | 7.438808e-01 |
| DIFF_PLT | TIME_BETWEE_PET | 0.043035123 | 7.462134e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_BMLR | -0.042477230 | 7.493951e-01 |
| DIFF_HB | DIFF_AST | 0.041854824 | 7.529498e-01 |
| DIFF_CRP | DIFF_eGFR | 0.041122916 | 7.571363e-01 |
| DIFF_RBC | DIFF_CRP | -0.040889695 | 7.584718e-01 |
| DIFF_BM_UPTAKE | DIFF_SLR | 0.040651713 | 7.598352e-01 |
| DIFF_PLT | DIFF_BMLR | 0.040185291 | 7.625096e-01 |
| DIFF_WBC | OVERALL_TIME | 0.038778175 | 7.705944e-01 |
| AGE | DIFF_SLR | -0.038722634 | 7.709141e-01 |
| DIFF_RBC | DIFF_K | -0.037850556 | 7.759376e-01 |
| DIFF_LDH | DIFF_BGL | -0.037709926 | 7.767485e-01 |
| CYCLES_BETWEEN_PET1_PET2 | OVERALL_TIME | 0.037361386 | 7.787594e-01 |
| DIFF_ALBUMIN | BMI | 0.037348764 | 7.788323e-01 |
| DIFF_RBC | DIFF_SPLEEN_UPTAKE | -0.036725270 | 7.824331e-01 |
| DIFF_AST | BMI | 0.036238237 | 7.852491e-01 |
| DIFF_WBC | DIFF_SPLEEN_UPTAKE | -0.034533152 | 7.951290e-01 |
| DIFF_CRP | DIFF_LDH | 0.034520556 | 7.952021e-01 |
| DIFF_LDH | BMI | -0.034121464 | 7.975193e-01 |
| DIFF_LIVER_UPTAKE | TIME_BETWEE_PET | 0.033922233 | 7.986768e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_eGFR | -0.033121337 | 8.033339e-01 |
| DIFF_LIVER_UPTAKE | dias | -0.033019413 | 8.039271e-01 |
| BMI | TIME_BETWEE_PET | 0.032398481 | 8.075431e-01 |
| DIFF_eGFR | DIFF_BM_UPTAKE | 0.031124425 | 8.149751e-01 |
| DIFF_BM_UPTAKE | TIME_BETWEE_PET | 0.031096289 | 8.151394e-01 |
| DIFF_HB | DIFF_ESTIMATED_SPLEEN_VOL | -0.030337572 | 8.195732e-01 |
| DIFF_BGL | BMI | 0.030209141 | 8.203243e-01 |
| DIFF_LDH | TIME_BETWEE_PET | 0.029999054 | 8.215533e-01 |
| DIFF_HB | DIFF_BM_UPTAKE | -0.029594014 | 8.239239e-01 |
| DIFF_eGFR | BMI | 0.029515766 | 8.243821e-01 |
| DIFF_WBC | DIFF_LDH | -0.028437404 | 8.307019e-01 |
| AGE | DIFF_LIVER_UPTAKE | 0.028000077 | 8.332679e-01 |
| AGE | OVERALL_TIME | 0.026043042 | 8.447718e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_ESTIMATED_SPLEEN_VOL | -0.025724648 | 8.466465e-01 |
| DIFF_ALBUMIN | DIFF_K | -0.025545845 | 8.476997e-01 |
| DIFF_PLT | DIFF_ALT | -0.025470540 | 8.481433e-01 |
| DIFF_SPLEEN_UPTAKE | DIFF_ESTIMATED_SPLEEN_VOL | -0.025398589 | 8.485672e-01 |
| DIFF_BGL | DIFF_ESTIMATED_SPLEEN_VOL | -0.024962002 | 8.511404e-01 |
| DIFF_ALBUMIN | dias | 0.023910418 | 8.573446e-01 |
| DIFF_BGL | dias | 0.023460185 | 8.600035e-01 |
| DIFF_BW | DIFF_SPLEEN_UPTAKE | -0.022970861 | 8.628950e-01 |
| DIFF_CRP | TIME_BETWEE_PET | -0.022441683 | 8.660241e-01 |
| DIFF_AST | DIFF_K | 0.021994564 | 8.686696e-01 |
| dias | OVERALL_TIME | 0.021761354 | 8.700499e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_SLR | 0.021614324 | 8.709204e-01 |
| AGE | TIME_BETWEE_PET | 0.020162783 | 8.795222e-01 |
| DIFF_ESTIMATED_SPLEEN_VOL | TIME_BETWEE_PET | 0.020094967 | 8.799244e-01 |
| DIFF_CRP | DIFF_BM_UPTAKE | -0.019743957 | 8.820068e-01 |
| DIFF_HB | OVERALL_TIME | 0.019727705 | 8.821032e-01 |
| DIFF_K | DIFF_SPLEEN_UPTAKE | 0.019088544 | 8.858971e-01 |
| DIFF_SLR | OVERALL_TIME | 0.017590295 | 8.948001e-01 |
| DIFF_ALT | DIFF_BW | -0.017573102 | 8.949023e-01 |
| DIFF_K | DIFF_BGL | 0.016663500 | 9.003139e-01 |
| DIFF_AST | dias | 0.016572664 | 9.008546e-01 |
| DIFF_CRP | BMI | -0.016265034 | 9.026860e-01 |
| DIFF_ALT | OVERALL_TIME | -0.015879750 | 9.049803e-01 |
| DIFF_ALBUMIN | OVERALL_TIME | -0.015119615 | 9.095093e-01 |
| AGE | BMI | 0.014950711 | 9.105160e-01 |
| DIFF_AST | TIME_BETWEE_PET | 0.014266294 | 9.145968e-01 |
| DIFF_LDH | DIFF_SLR | 0.013268646 | 9.205491e-01 |
| DIFF_ALBUMIN | DIFF_AST | 0.013151358 | 9.212492e-01 |
| DIFF_eGFR | DIFF_SLR | 0.011981648 | 9.282341e-01 |
| AGE | DIFF_LDH | 0.010928728 | 9.345263e-01 |
| AGE | DIFF_AST | 0.010708650 | 9.358419e-01 |
| DIFF_BGL | DIFF_BW | -0.010689323 | 9.359575e-01 |
| DIFF_LIVER_UPTAKE | DIFF_BMLR | -0.010053041 | 9.397623e-01 |
| DIFF_ALT | DIFF_K | 0.009469946 | 9.432503e-01 |
| DIFF_RBC | DIFF_ALT | 0.008475072 | 9.492039e-01 |
| AGE | DIFF_WBC | -0.008148403 | 9.511594e-01 |
| DIFF_ALT | TIME_BETWEE_PET | 0.007030515 | 9.578533e-01 |
| DIFF_K | DIFF_LIVER_UPTAKE | 0.005641048 | 9.661775e-01 |
| DIFF_WBC | DIFF_BGL | -0.004691474 | 9.718684e-01 |
| DIFF_ALBUMIN | DIFF_BMLR | 0.003521538 | 9.788819e-01 |
| DIFF_BGL | TIME_BETWEE_PET | 0.002856731 | 9.828679e-01 |
| AGE | DIFF_ALBUMIN | -0.002662533 | 9.840324e-01 |
| CYCLES_BETWEEN_PET1_PET2 | DIFF_PLT | -0.001665029 | 9.900142e-01 |
| DIFF_WBC | DIFF_BM_UPTAKE | -0.001475875 | 9.911486e-01 |
| DIFF_K | dias | 0.001458314 | 9.912539e-01 |
# Todas la tablas de frecuencias:
all_var <- univar_category(datos)
all_var
| STATUS | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| ALIVE | 38 | 0.6440678 |
| DEATH | 21 | 0.3559322 |
| GENDER | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| FEMALE | 20 | 0.3389831 |
| MALE | 39 | 0.6610169 |
| TNM_STAGE | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| I | 6 | 0.1016949 |
| II | 15 | 0.2542373 |
| III | 26 | 0.4406780 |
| IV | 12 | 0.2033898 |
| DIAGNOSTIC | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| EWING SARCOMA | 2 | 0.03389831 |
| GASTRIC CANCER | 3 | 0.05084746 |
| GINECOLOGICAL | 7 | 0.11864407 |
| HEAD AND NECK | 10 | 0.16949153 |
| LUNG CANCER | 19 | 0.32203390 |
| MELANOMA | 5 | 0.08474576 |
| PANCREAS CANCER | 7 | 0.11864407 |
| RENAL CANCER | 4 | 0.06779661 |
| SARCOMA | 1 | 0.01694915 |
| UROTHELIAL CARCINOMA | 1 | 0.01694915 |
| TREATMENT | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| CHEMO | 37 | 0.6271186 |
| ICI | 22 | 0.3728814 |
| ECOGPS | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| ASYMTOMATIC | 26 | 0.44067797 |
| BEDBOUND | 1 | 0.01694915 |
| SYMPTOMATIC >50 % IN THE BED | 1 | 0.01694915 |
| SYMPTOMATIC BUT AMBULATORY | 17 | 0.28813559 |
| SYMPTOMATIC,<50% IN BED DURING THE DAY | 14 | 0.23728814 |
| COMORBIDITIES | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| ATEROESCLEROSIS | 3 | 0.05084746 |
| CANCER RRECURRENCE | 8 | 0.13559322 |
| CHRONIC INFLAMMATION | 9 | 0.15254237 |
| DIABETES MELLITUS | 13 | 0.22033898 |
| HYPERLIPIDEMIA | 12 | 0.20338983 |
| HYPERTENSION | 14 | 0.23728814 |
| CTCNCI | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| HIGHT SEVERE | 4 | 0.06779661 |
| MODERATE | 11 | 0.18644068 |
| NO SIDE EFFECTS | 28 | 0.47457627 |
| SEVERE | 12 | 0.20338983 |
| SLIGTHLY SIDE EFFCTS | 4 | 0.06779661 |
| ACTION_TAKEN_ | n | rate |
|---|---|---|
| <fct> | <int> | <dbl> |
| ADDED OTHER TREATMEN | 3 | 0.05084746 |
| ADDED STEROIDS | 1 | 0.01694915 |
| DOSE NOT CHANGED | 21 | 0.35593220 |
| DOSE REDUCED | 17 | 0.28813559 |
| DRUG INTERRUPTED | 9 | 0.15254237 |
| DRUG WIHDRAWN | 1 | 0.01694915 |
| UNKNOW | 7 | 0.11864407 |
# Diagrama de barras por separado variables cualitativas:
plot(all_var)
# Todos los diagramas de barra juntos:
plot_bar_category(datos)
# Tabla de frecuencias + diagrama de barras + diagrama de sectores en una iteración:
vec_var_cual <- c('GENDER','TNM_STAGE','DIAGNOSTIC','TREATMENT','ECOGPS','COMORBIDITIES','CTCNCI','ACTION_TAKEN_')
for (i in vec_var_cual) {
# 0. Seleccionar la variable con el contador i:
#***************************************************************
var_i = i
print("********************************************")
print(var_i)
print("********************************************")
# 1. Creo la tabla de frecuencias relativas y absolutas:
#***************************************************************
# Tabla de frecuencias absolutas:
tabla_frec = data.frame(t(table(datos[,var_i])))[,2:3]
colnames(tabla_frec)<-c("Grupos","Frec_Absoluta")
# Tabla de frecuencias relativas:
tabla_frec$Frec_Relativa <- tabla_frec$Frec_Absoluta/sum(tabla_frec$Frec_Absoluta)
print(tabla_frec)
# 3. Crear el diagrama de sectores
#***************************************************************
data <- data.frame(
group=tabla_frec$Grupos,
value=tabla_frec$Frec_Relativa
)
data <- data %>%
arrange(desc(group)) %>%
mutate(prop = value / sum(data$value) *100) %>%
mutate(ypos = cumsum(prop)- 0.5*prop )
p2 <- ggplot(data, aes(x="", y=prop, fill=group)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0) +
theme_void() +
theme(legend.position="none") +
geom_text(aes(y = ypos, label = group), color = "white", size=6) +
scale_fill_brewer(palette="Set1")
# 2. Crear el barplot frecuencia Absoluta
#***************************************************************
p1 <- ggplot(data=tabla_frec, aes(x=Grupos, y=Frec_Absoluta)) +
geom_bar(stat="identity", fill="steelblue")+
geom_text(aes(label=Frec_Relativa), vjust=1.6, color="black", size=5)+
theme_minimal()
grid.arrange(p1, p2,
nrow=2, ncol = 1,
top = textGrob(var_i,gp=gpar(fontsize=16,font=3)))
}
[1] "********************************************" [1] "GENDER" [1] "********************************************" Grupos Frec_Absoluta Frec_Relativa 1 FEMALE 20 0.3389831 2 MALE 39 0.6610169 [1] "********************************************" [1] "TNM_STAGE" [1] "********************************************" Grupos Frec_Absoluta Frec_Relativa 1 I 6 0.1016949 2 II 15 0.2542373 3 III 26 0.4406780 4 IV 12 0.2033898
[1] "********************************************"
[1] "DIAGNOSTIC"
[1] "********************************************"
Grupos Frec_Absoluta Frec_Relativa
1 EWING SARCOMA 2 0.03389831
2 GASTRIC CANCER 3 0.05084746
3 GINECOLOGICAL 7 0.11864407
4 HEAD AND NECK 10 0.16949153
5 LUNG CANCER 19 0.32203390
6 MELANOMA 5 0.08474576
7 PANCREAS CANCER 7 0.11864407
8 RENAL CANCER 4 0.06779661
9 SARCOMA 1 0.01694915
10 UROTHELIAL CARCINOMA 1 0.01694915
[1] "********************************************" [1] "TREATMENT" [1] "********************************************" Grupos Frec_Absoluta Frec_Relativa 1 CHEMO 37 0.6271186 2 ICI 22 0.3728814
[1] "********************************************"
[1] "ECOGPS"
[1] "********************************************"
Grupos Frec_Absoluta Frec_Relativa
1 ASYMTOMATIC 26 0.44067797
2 BEDBOUND 1 0.01694915
3 SYMPTOMATIC >50 % IN THE BED 1 0.01694915
4 SYMPTOMATIC BUT AMBULATORY 17 0.28813559
5 SYMPTOMATIC,<50% IN BED DURING THE DAY 14 0.23728814
[1] "********************************************"
[1] "COMORBIDITIES"
[1] "********************************************"
Grupos Frec_Absoluta Frec_Relativa
1 ATEROESCLEROSIS 3 0.05084746
2 CANCER RRECURRENCE 8 0.13559322
3 CHRONIC INFLAMMATION 9 0.15254237
4 DIABETES MELLITUS 13 0.22033898
5 HYPERLIPIDEMIA 12 0.20338983
6 HYPERTENSION 14 0.23728814
[1] "********************************************"
[1] "CTCNCI"
[1] "********************************************"
Grupos Frec_Absoluta Frec_Relativa
1 HIGHT SEVERE 4 0.06779661
2 MODERATE 11 0.18644068
3 NO SIDE EFFECTS 28 0.47457627
4 SEVERE 12 0.20338983
5 SLIGTHLY SIDE EFFCTS 4 0.06779661
[1] "********************************************"
[1] "ACTION_TAKEN_"
[1] "********************************************"
Grupos Frec_Absoluta Frec_Relativa
1 ADDED OTHER TREATMEN 3 0.05084746
2 ADDED STEROIDS 1 0.01694915
3 DOSE NOT CHANGED 21 0.35593220
4 DOSE REDUCED 17 0.28813559
5 DRUG INTERRUPTED 9 0.15254237
6 DRUG WIHDRAWN 1 0.01694915
7 UNKNOW 7 0.11864407
# DESCRIPCION MULTIVARIADA: las variables en función de la respuesta
name_y <- as.name(respuesta[1])
descripcion_num <- datos %>%
group_by(!!name_y)%>%
dlookr::describe()
descripcion_num
| described_variables | STATUS | n | na | mean | sd | se_mean | IQR | skewness | kurtosis | p00 | p01 | p05 | p10 | p20 | p25 | p30 | p40 | p50 | p60 | p70 | p75 | p80 | p90 | p95 | p99 | p100 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <fct> | <int> | <int> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| AGE | ALIVE | 38 | 0 | 68.552631579 | 8.1562358 | 1.32311616 | 12.2500000 | -0.35796368 | -0.48367838 | 52.0000000 | 52.7400000 | 54.0000000 | 55.7000000 | 60.40000000 | 62.50000000 | 65.00000000 | 68.000000000 | 70.000000000 | 72.00000000 | 74.0000000 | 74.7500000 | 75.6000000 | 76.3000000 | 79.0000000 | 83.4100000 | 86.0000000 |
| AGE | DEATH | 21 | 0 | 70.904761905 | 9.4228698 | 2.05623878 | 14.0000000 | -0.14495828 | -0.82993240 | 53.0000000 | 53.6000000 | 56.0000000 | 60.0000000 | 63.00000000 | 64.00000000 | 67.00000000 | 68.000000000 | 70.000000000 | 74.00000000 | 77.0000000 | 78.0000000 | 79.0000000 | 83.0000000 | 83.0000000 | 86.2000000 | 87.0000000 |
| BMI | ALIVE | 38 | 0 | 22.135789474 | 4.2896479 | 0.69587278 | 5.3525000 | 0.88974456 | 1.40256997 | 15.4300000 | 15.7297000 | 16.4355000 | 17.1050000 | 18.22400000 | 18.83250000 | 20.12300000 | 20.808000000 | 21.825000000 | 22.83200000 | 23.5790000 | 24.1850000 | 25.7080000 | 26.9140000 | 29.1775000 | 33.4975000 | 35.8100000 |
| BMI | DEATH | 21 | 0 | 20.474761905 | 3.3661545 | 0.73455514 | 5.6200000 | -0.05915208 | -1.07758686 | 14.0800000 | 14.4980000 | 16.1700000 | 16.6800000 | 17.30000000 | 17.89000000 | 18.07000000 | 19.260000000 | 20.070000000 | 21.79000000 | 23.1400000 | 23.5100000 | 23.7600000 | 24.4600000 | 25.2100000 | 25.6660000 | 25.7800000 |
| CYCLES_BETWEEN_PET1_PET2 | ALIVE | 38 | 0 | 7.368421053 | 8.8330493 | 1.43290981 | 3.7500000 | 3.23904673 | 12.32718502 | 2.0000000 | 2.0000000 | 2.0000000 | 2.0000000 | 3.00000000 | 3.00000000 | 3.00000000 | 4.000000000 | 4.000000000 | 5.00000000 | 6.0000000 | 6.7500000 | 11.2000000 | 14.0000000 | 21.3500000 | 40.9700000 | 48.0000000 |
| CYCLES_BETWEEN_PET1_PET2 | DEATH | 21 | 0 | 6.523809524 | 4.0201872 | 0.87727676 | 4.0000000 | 1.58544523 | 2.70180500 | 2.0000000 | 2.0000000 | 2.0000000 | 3.0000000 | 4.00000000 | 4.00000000 | 4.00000000 | 5.000000000 | 6.000000000 | 6.00000000 | 7.0000000 | 8.0000000 | 9.0000000 | 10.0000000 | 15.0000000 | 17.4000000 | 18.0000000 |
| dias | ALIVE | 38 | 0 | 46.842105263 | 137.8935207 | 22.36928289 | 23.7500000 | 4.06563184 | 17.78192579 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.000000000 | 2.00000000 | 14.9000000 | 23.7500000 | 30.4000000 | 64.2000000 | 319.6500000 | 597.3600000 | 735.0000000 |
| dias | DEATH | 21 | 0 | 75.809523810 | 127.5623844 | 27.83639439 | 86.0000000 | 1.95190402 | 3.08842106 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.00000000 | 0.00000000 | 0.00000000 | 3.000000000 | 7.000000000 | 33.00000000 | 52.0000000 | 86.0000000 | 114.0000000 | 233.0000000 | 364.0000000 | 425.6000000 | 441.0000000 |
| DIFF_ALBUMIN | ALIVE | 38 | 0 | -0.057631579 | 0.6964948 | 0.11298638 | 0.4625000 | 0.30469934 | 1.31440247 | -1.7000000 | -1.6260000 | -1.2620000 | -0.6430000 | -0.48400000 | -0.29500000 | -0.23700000 | -0.168000000 | -0.120000000 | -0.06600000 | 0.0500000 | 0.1675000 | 0.2200000 | 1.0310000 | 1.2025000 | 1.5883000 | 1.7400000 |
| DIFF_ALBUMIN | DEATH | 21 | 0 | -0.057619048 | 0.9679251 | 0.21121858 | 0.2500000 | 0.88964699 | 4.51502526 | -2.2800000 | -2.0940000 | -1.3500000 | -0.9000000 | -0.37000000 | -0.17000000 | -0.16000000 | -0.110000000 | -0.060000000 | -0.05000000 | 0.0500000 | 0.0800000 | 0.1000000 | 0.2000000 | 1.5700000 | 2.5780000 | 2.8300000 |
| DIFF_ALT | ALIVE | 38 | 0 | -5.223684211 | 15.9310412 | 2.58435614 | 10.2250000 | -3.38559938 | 16.87827941 | -84.7000000 | -62.2040000 | -19.9900000 | -15.1500000 | -10.08000000 | -9.20000000 | -6.67000000 | -4.640000000 | -2.250000000 | -1.10000000 | 0.5500000 | 1.0250000 | 2.0400000 | 5.2300000 | 13.7400000 | 17.7450000 | 18.3000000 |
| DIFF_ALT | DEATH | 21 | 0 | -17.208571429 | 57.1424671 | 12.46950861 | 9.4200000 | -3.82284716 | 15.51173073 | -250.8000000 | -217.6200000 | -84.9000000 | -16.9000000 | -9.20000000 | -8.80000000 | -8.60000000 | -6.000000000 | -2.600000000 | 0.00000000 | 0.4000000 | 0.6200000 | 3.3000000 | 7.5000000 | 14.3000000 | 18.3800000 | 19.4000000 |
| DIFF_AST | ALIVE | 38 | 0 | -4.778947368 | 19.2680124 | 3.12568435 | 9.0250000 | -4.78306274 | 26.77840367 | -111.5000000 | -77.5340000 | -18.8500000 | -12.6100000 | -7.14000000 | -6.55000000 | -5.51000000 | -3.120000000 | -0.700000000 | 0.02000000 | 1.1400000 | 2.4750000 | 4.3600000 | 5.0600000 | 7.3050000 | 14.8300000 | 18.9000000 |
| DIFF_AST | DEATH | 21 | 0 | -11.800476190 | 49.7210238 | 10.85001691 | 12.0000000 | -4.03400147 | 17.16036256 | -219.6000000 | -186.8600000 | -55.9000000 | -11.6000000 | -5.80000000 | -5.50000000 | -3.50000000 | -1.100000000 | -0.800000000 | 0.00000000 | 1.7000000 | 6.5000000 | 6.6000000 | 11.9000000 | 13.2000000 | 15.1200000 | 15.6000000 |
| DIFF_BGL | ALIVE | 38 | 0 | -3.315789474 | 43.2723026 | 7.01969443 | 27.5000000 | -1.63834398 | 4.74218790 | -160.0000000 | -144.4600000 | -77.2000000 | -45.5000000 | -13.60000000 | -12.75000000 | -10.70000000 | -4.600000000 | 2.000000000 | 9.40000000 | 12.0000000 | 14.7500000 | 19.6000000 | 30.4000000 | 64.0000000 | 67.7800000 | 70.0000000 |
| DIFF_BGL | DEATH | 21 | 0 | 2.428571429 | 22.6728283 | 4.94761676 | 23.0000000 | -1.62777362 | 4.23786443 | -70.0000000 | -61.4000000 | -27.0000000 | -18.0000000 | -6.00000000 | -6.00000000 | -2.00000000 | -1.000000000 | 1.000000000 | 14.00000000 | 16.0000000 | 17.0000000 | 17.0000000 | 29.0000000 | 29.0000000 | 29.8000000 | 30.0000000 |
| DIFF_BM_UPTAKE | ALIVE | 38 | 0 | 0.100000000 | 0.6192498 | 0.10045558 | 0.7750000 | 0.47868759 | 1.70780206 | -1.2000000 | -1.1297000 | -0.8315000 | -0.6480000 | -0.34800000 | -0.28000000 | -0.19800000 | -0.028000000 | 0.095000000 | 0.20200000 | 0.4390000 | 0.4950000 | 0.5740000 | 0.7210000 | 0.8490000 | 1.6434000 | 2.0800000 |
| DIFF_BM_UPTAKE | DEATH | 21 | 0 | 0.120000000 | 0.5834724 | 0.12732411 | 0.6000000 | 0.97872356 | 2.56877396 | -0.8200000 | -0.8000000 | -0.7200000 | -0.4500000 | -0.40000000 | -0.21000000 | -0.19000000 | 0.010000000 | 0.200000000 | 0.24000000 | 0.3500000 | 0.3900000 | 0.4100000 | 0.7000000 | 0.7400000 | 1.6120000 | 1.8300000 |
| DIFF_BMLR | ALIVE | 38 | 0 | 0.005877421 | 0.2736999 | 0.04439999 | 0.3242798 | 0.06548622 | 0.10607508 | -0.6192344 | -0.5805216 | -0.3880261 | -0.3188116 | -0.20431746 | -0.16686314 | -0.13508456 | -0.064767496 | 0.006350011 | 0.07472226 | 0.1402702 | 0.1574167 | 0.1849698 | 0.3389879 | 0.4989430 | 0.5797461 | 0.6197762 |
| DIFF_BMLR | DEATH | 21 | 0 | 0.072130571 | 0.3516416 | 0.07673450 | 0.3824610 | 0.42492308 | 1.98629788 | -0.6207071 | -0.6042323 | -0.5383333 | -0.3211071 | -0.09719522 | -0.09373885 | -0.07072829 | 0.002627451 | 0.081944444 | 0.09726844 | 0.2001239 | 0.2887222 | 0.2893665 | 0.3768175 | 0.4389387 | 0.9038135 | 1.0200322 |
| DIFF_BW | ALIVE | 38 | 0 | 0.142105263 | 6.2442030 | 1.01294348 | 6.6500000 | 0.72112651 | 0.63190779 | -11.1000000 | -10.9890000 | -8.5050000 | -6.3200000 | -4.86000000 | -3.60000000 | -2.38000000 | -1.300000000 | -0.550000000 | 0.00000000 | 0.8000000 | 3.0500000 | 4.5600000 | 9.0200000 | 12.6350000 | 15.4150000 | 15.6000000 |
| DIFF_BW | DEATH | 21 | 0 | 8.172380952 | 11.3566201 | 2.47821767 | 4.4000000 | 1.94716942 | 4.16313988 | -4.3000000 | -3.9000000 | -2.3000000 | -1.5000000 | 0.00000000 | 3.10000000 | 3.60000000 | 4.600000000 | 5.300000000 | 5.90000000 | 6.9000000 | 7.5000000 | 8.7000000 | 24.1000000 | 26.2000000 | 40.4560000 | 44.0200000 |
| DIFF_CRP | ALIVE | 38 | 0 | 0.586578947 | 4.5745998 | 0.74209809 | 0.4450000 | 2.28771118 | 7.20120744 | -7.2300000 | -6.9155000 | -4.2550000 | -3.0580000 | -0.52000000 | -0.33750000 | -0.10000000 | -0.052000000 | -0.010000000 | 0.02000000 | 0.0400000 | 0.1075000 | 0.1800000 | 4.6960000 | 8.6990000 | 16.8606000 | 17.8300000 |
| DIFF_CRP | DEATH | 21 | 0 | -0.123809524 | 4.1593082 | 0.90763546 | 1.1800000 | -2.65479802 | 11.12447049 | -15.8800000 | -13.2320000 | -2.6400000 | -2.0300000 | -0.22000000 | -0.15000000 | -0.02000000 | 0.030000000 | 0.250000000 | 0.32000000 | 0.3900000 | 1.0300000 | 1.1900000 | 2.9600000 | 3.0200000 | 6.3720000 | 7.2100000 |
| DIFF_eGFR | ALIVE | 38 | 0 | -1.621052632 | 15.5613374 | 2.52438226 | 14.5000000 | 1.01248285 | 2.01317072 | -30.1000000 | -28.2500000 | -24.2500000 | -17.3400000 | -12.98000000 | -10.22500000 | -9.38000000 | -7.820000000 | -3.150000000 | 0.08000000 | 2.8900000 | 4.2750000 | 8.0200000 | 14.9400000 | 25.2900000 | 42.7420000 | 47.7000000 |
| DIFF_eGFR | DEATH | 21 | 0 | -4.085714286 | 18.1665430 | 3.96426469 | 17.0000000 | 0.37991024 | 1.92431183 | -39.2000000 | -37.9000000 | -32.7000000 | -26.4000000 | -19.60000000 | -10.20000000 | -6.30000000 | -4.600000000 | -2.600000000 | -0.90000000 | 3.8000000 | 6.8000000 | 7.5000000 | 9.5000000 | 9.9000000 | 38.2200000 | 45.3000000 |
| DIFF_ESTIMATED_SPLEEN_VOL | ALIVE | 38 | 0 | 6.955263158 | 55.4416796 | 8.99382805 | 46.5750000 | 0.38060194 | 1.62630342 | -110.2000000 | -101.8380000 | -85.3900000 | -63.1900000 | -42.22000000 | -15.85000000 | -1.72000000 | 3.800000000 | 8.600000000 | 18.20000000 | 26.2600000 | 30.7250000 | 38.3600000 | 59.4800000 | 98.8900000 | 148.2880000 | 177.0000000 |
| DIFF_ESTIMATED_SPLEEN_VOL | DEATH | 21 | 0 | 11.623809524 | 39.4728502 | 8.61368210 | 37.1000000 | -0.72159619 | 1.29425090 | -85.5000000 | -82.1000000 | -68.5000000 | -20.2000000 | -5.60000000 | -3.10000000 | -2.40000000 | 2.400000000 | 15.100000000 | 24.00000000 | 33.1000000 | 34.0000000 | 34.2000000 | 55.2000000 | 65.2000000 | 80.4000000 | 84.2000000 |
| DIFF_HB | ALIVE | 38 | 0 | 0.169473684 | 2.1120644 | 0.34262208 | 2.8250000 | 0.51856836 | 0.08700801 | -3.5000000 | -3.3890000 | -2.6900000 | -2.2300000 | -1.82000000 | -1.45000000 | -0.89000000 | -0.420000000 | -0.100000000 | 0.44000000 | 1.1800000 | 1.3750000 | 1.7600000 | 2.8820000 | 3.7150000 | 5.0600000 | 5.8000000 |
| DIFF_HB | DEATH | 21 | 0 | -0.576190476 | 2.1030703 | 0.45892757 | 2.1000000 | 0.04091279 | 0.50400745 | -5.2000000 | -4.8600000 | -3.5000000 | -2.8000000 | -1.60000000 | -1.50000000 | -1.20000000 | -1.100000000 | -0.900000000 | -0.60000000 | 0.4000000 | 0.6000000 | 0.9000000 | 1.9000000 | 2.4000000 | 3.6800000 | 4.0000000 |
| DIFF_K | ALIVE | 38 | 0 | 0.107894737 | 0.3612004 | 0.05859444 | 0.4125000 | 0.14853719 | -0.08333286 | -0.6700000 | -0.6293000 | -0.4835000 | -0.2810000 | -0.18200000 | -0.10000000 | -0.06600000 | 0.048000000 | 0.075000000 | 0.14000000 | 0.2390000 | 0.3125000 | 0.3600000 | 0.6170000 | 0.7345000 | 0.8356000 | 0.8800000 |
| DIFF_K | DEATH | 21 | 0 | 0.220476190 | 0.4896067 | 0.10684095 | 0.5100000 | -0.52745476 | 0.22208083 | -0.9000000 | -0.8480000 | -0.6400000 | -0.3400000 | -0.01000000 | 0.00000000 | 0.00000000 | 0.140000000 | 0.250000000 | 0.44000000 | 0.4800000 | 0.5100000 | 0.5900000 | 0.7300000 | 0.9100000 | 1.0380000 | 1.0700000 |
| DIFF_LDH | ALIVE | 38 | 0 | -9.547368421 | 95.2511410 | 15.45177545 | 52.2500000 | -2.30390086 | 5.50679173 | -333.0000000 | -318.5700000 | -274.4500000 | -70.5000000 | -23.20000000 | -14.50000000 | -11.30000000 | 0.400000000 | 8.500000000 | 17.00000000 | 29.5000000 | 37.7500000 | 43.4000000 | 67.6000000 | 86.7500000 | 98.5600000 | 103.0000000 |
| DIFF_LDH | DEATH | 21 | 0 | 9.428571429 | 170.0248721 | 37.10246888 | 41.0000000 | -0.38842221 | 6.26609952 | -510.0000000 | -436.0000000 | -140.0000000 | -65.0000000 | -23.00000000 | -9.00000000 | -8.00000000 | -4.000000000 | 5.000000000 | 5.00000000 | 18.0000000 | 32.0000000 | 34.0000000 | 170.0000000 | 194.0000000 | 416.4000000 | 472.0000000 |
| DIFF_LIVER_UPTAKE | ALIVE | 38 | 0 | 0.094736842 | 0.5093755 | 0.08263162 | 0.7125000 | -1.03381469 | 1.86159126 | -1.5900000 | -1.3162000 | -0.7225000 | -0.4950000 | -0.25800000 | -0.21500000 | -0.09800000 | 0.018000000 | 0.120000000 | 0.27800000 | 0.4580000 | 0.4975000 | 0.5500000 | 0.6160000 | 0.6785000 | 0.8652000 | 0.8800000 |
| DIFF_LIVER_UPTAKE | DEATH | 21 | 0 | -0.121428571 | 0.4632201 | 0.10108291 | 0.6400000 | 0.13873995 | -0.73626526 | -0.8700000 | -0.8640000 | -0.8400000 | -0.7400000 | -0.38000000 | -0.37000000 | -0.35000000 | -0.320000000 | -0.160000000 | -0.03000000 | 0.1900000 | 0.2700000 | 0.3000000 | 0.5100000 | 0.5200000 | 0.7200000 | 0.7700000 |
| DIFF_PLT | ALIVE | 38 | 0 | -26.710526316 | 121.7067605 | 19.74344366 | 87.7500000 | 0.20395211 | 2.11887063 | -354.0000000 | -309.2300000 | -229.6000000 | -166.2000000 | -89.80000000 | -77.25000000 | -60.90000000 | -56.400000000 | -18.000000000 | -8.00000000 | 3.6000000 | 10.5000000 | 19.2000000 | 96.5000000 | 180.8000000 | 288.6200000 | 316.0000000 |
| DIFF_PLT | DEATH | 21 | 0 | 11.047619048 | 141.2085253 | 30.81422648 | 58.0000000 | 1.31681596 | 5.07388455 | -258.0000000 | -244.2000000 | -189.0000000 | -151.0000000 | -40.00000000 | -24.00000000 | -12.00000000 | -9.000000000 | 4.000000000 | 16.00000000 | 31.0000000 | 34.0000000 | 44.0000000 | 79.0000000 | 197.0000000 | 409.8000000 | 463.0000000 |
| DIFF_RBC | ALIVE | 38 | 0 | -0.280000000 | 0.7164439 | 0.11622255 | 0.9000000 | -0.04813573 | -0.38757697 | -1.9300000 | -1.7006000 | -1.3015000 | -1.2480000 | -0.88400000 | -0.76750000 | -0.67600000 | -0.452000000 | -0.260000000 | 0.03200000 | 0.0970000 | 0.1325000 | 0.1640000 | 0.8020000 | 0.8475000 | 1.0538000 | 1.1500000 |
| DIFF_RBC | DEATH | 21 | 0 | -0.367142857 | 0.6828627 | 0.14901285 | 0.6500000 | -0.40499884 | 0.30518589 | -1.9700000 | -1.8640000 | -1.4400000 | -1.1100000 | -0.86000000 | -0.77000000 | -0.52000000 | -0.410000000 | -0.290000000 | -0.28000000 | -0.1900000 | -0.1200000 | 0.1300000 | 0.5900000 | 0.6300000 | 0.6940000 | 0.7100000 |
| DIFF_SLR | ALIVE | 38 | 0 | 0.012379763 | 0.1893314 | 0.03071361 | 0.2242893 | 0.39905069 | 0.35975745 | -0.3187647 | -0.3172185 | -0.2956006 | -0.2349369 | -0.15802676 | -0.11211673 | -0.09112568 | -0.029369579 | 0.015962444 | 0.05086431 | 0.0870160 | 0.1121725 | 0.1251329 | 0.2291428 | 0.3278227 | 0.4737786 | 0.5090979 |
| DIFF_SLR | DEATH | 21 | 0 | -0.014920417 | 0.2696536 | 0.05884323 | 0.2990705 | 0.40533483 | 1.41268415 | -0.5135982 | -0.5113946 | -0.5025806 | -0.2566667 | -0.19362704 | -0.13682278 | -0.13516976 | -0.101647059 | -0.045342594 | 0.03066528 | 0.1359975 | 0.1622477 | 0.2076389 | 0.2268707 | 0.2537146 | 0.6017427 | 0.6887497 |
| DIFF_SPLEEN_UPTAKE | ALIVE | 38 | 0 | 0.042105263 | 0.3802855 | 0.06169046 | 0.3600000 | 0.11739142 | 1.12480748 | -0.8000000 | -0.7482000 | -0.6345000 | -0.4380000 | -0.18400000 | -0.12000000 | -0.07900000 | -0.010000000 | 0.055000000 | 0.15800000 | 0.2180000 | 0.2400000 | 0.2840000 | 0.4180000 | 0.6175000 | 0.9561000 | 1.1300000 |
| DIFF_SPLEEN_UPTAKE | DEATH | 21 | 0 | 0.066190476 | 0.3390940 | 0.07399638 | 0.3500000 | 1.30365796 | 3.95311040 | -0.4200000 | -0.4180000 | -0.4100000 | -0.4000000 | -0.14000000 | -0.13000000 | -0.07000000 | -0.050000000 | 0.050000000 | 0.10000000 | 0.2000000 | 0.2200000 | 0.2700000 | 0.3400000 | 0.3400000 | 0.9720000 | 1.1300000 |
| DIFF_WBC | ALIVE | 38 | 0 | -0.468421053 | 2.6410159 | 0.42842935 | 3.9525000 | 0.27158719 | -0.72429442 | -5.7000000 | -5.3596000 | -3.9215000 | -3.1980000 | -2.67600000 | -2.42750000 | -2.14500000 | -1.656000000 | -0.855000000 | -0.16000000 | 1.1160000 | 1.5250000 | 1.8840000 | 3.5310000 | 3.9410000 | 4.4127000 | 4.5200000 |
| DIFF_WBC | DEATH | 21 | 0 | 0.355238095 | 2.8624982 | 0.62464833 | 3.3500000 | 0.85520225 | 1.17189325 | -4.6400000 | -4.2480000 | -2.6800000 | -2.6700000 | -1.94000000 | -1.29000000 | -1.23000000 | -0.360000000 | 0.100000000 | 0.38000000 | 1.1300000 | 2.0600000 | 2.3700000 | 3.2200000 | 5.4800000 | 7.2880000 | 7.7400000 |
| OVERALL_TIME | ALIVE | 38 | 0 | 17.763157895 | 16.2537883 | 2.63671263 | 16.5000000 | 1.49609275 | 1.96731456 | 1.0000000 | 1.0000000 | 1.8500000 | 2.7000000 | 4.40000000 | 6.00000000 | 7.20000000 | 11.000000000 | 14.500000000 | 15.20000000 | 20.0000000 | 22.5000000 | 25.8000000 | 41.0000000 | 51.9500000 | 64.2600000 | 65.0000000 |
| OVERALL_TIME | DEATH | 21 | 0 | 21.523809524 | 15.1480000 | 3.30556461 | 17.0000000 | 1.30493144 | 1.91970514 | 1.0000000 | 1.6000000 | 4.0000000 | 8.0000000 | 12.00000000 | 12.00000000 | 13.00000000 | 16.000000000 | 17.000000000 | 21.00000000 | 23.0000000 | 29.0000000 | 30.0000000 | 42.0000000 | 47.0000000 | 60.6000000 | 64.0000000 |
| TIME_BETWEE_PET | ALIVE | 38 | 0 | 10.578947368 | 8.9703162 | 1.45517743 | 11.5000000 | 1.40520467 | 1.58539181 | 1.0000000 | 1.3700000 | 2.0000000 | 2.7000000 | 4.00000000 | 4.00000000 | 5.00000000 | 6.000000000 | 7.500000000 | 9.00000000 | 11.8000000 | 15.5000000 | 18.2000000 | 22.2000000 | 26.2000000 | 36.1500000 | 38.0000000 |
| TIME_BETWEE_PET | DEATH | 21 | 0 | 7.904761905 | 3.9484777 | 0.86162847 | 7.0000000 | -0.18573481 | -0.95771678 | 1.0000000 | 1.2000000 | 2.0000000 | 3.0000000 | 4.00000000 | 4.00000000 | 5.00000000 | 8.000000000 | 9.000000000 | 9.00000000 | 10.0000000 | 11.0000000 | 11.0000000 | 12.0000000 | 13.0000000 | 14.6000000 | 15.0000000 |
# Diagrama de dispersion por pareja
options(repr.plot.width=8, repr.plot.height=5)
var_respuesta <- as.name(respuesta)
for (i in 1:length(var_num)) {
name_i <- as.name(var_num[i])
r <- list()
r[[1]] <- ggplot(datos, aes(x=!!name_i, y=!!var_respuesta)) +
geom_point() +
geom_smooth(method=lm ,formula = y ~ x, color="red", fill="#69b3a2", se=TRUE) +
theme_minimal()
grid.arrange(r[[1]],
nrow=1, ncol = 1,
top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}
options(repr.plot.width=16, repr.plot.height=8)
# boxplot + diagrama de error + histograma de densidad + violin plot
for (i in 1:length(var_num)) {
name_i <- as.name(var_num[i])
name_y <- as.name(respuesta[1])
r <- list()
# Histograma de densidad
r[[1]]<-ggplot(datos, aes(x=!!name_i, fill=!!name_y)) +
geom_density(alpha=0.4)+
theme_light() +
#theme(legend.position = "none") +
xlab("") +
ylab("Densidad de Frecuencia")
# Boxplot
r[[2]]<-ggplot(data=datos, aes(y=!!name_i, x=!!name_y)) +
geom_boxplot(size = 0.4) +
theme_light() +
theme(legend.position = "none")+
xlab("")
# Violin plot con diagramas de error
r[[3]]<-ggerrorplot(data = datos, x = respuesta, y = var_num[i],
desc_stat = "mean_ci",
error.plot = "errorbar",
add = c("violin","mean"))+
theme_light() +
theme(legend.position = "none") +
xlab("") +
ylab("") +
#ggtitle("Comparacion pvut") +
stat_compare_means(comparisons = c("0","1"))+
stat_compare_means(label.y = 1.05*max(datos[,var_num[i]]))
grid.arrange(r[[1]], r[[2]], r[[3]],
nrow=3, ncol = 1,
top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}
name_y <- as.name(respuesta[1])
datos %>%
group_by(!!name_y) %>%
plot_bar_category()
# EFECTOS SECUNDARIOS POR TRATAMIENTO(podemos hacer todas las combinaciones posibles)
var_cual_x = "CTCNCI"
var_cual_y = "TREATMENT"
name_x = as.name(var_cual_x)
name_y = as.name(var_cual_y)
ggbarstats(datos, x = !!name_x, y = !!name_y)
# ACCION DEL MEDICO POR TRATAMIENTO
var_cual_x = "TREATMENT"
var_cual_y = "ACTION_TAKEN_"
name_x = as.name(var_cual_x)
name_y = as.name(var_cual_y)
ggbarstats(datos, x = !!name_x, y = !!name_y)
# Grafico comparativo de : efectos adversos con el tratamiento,junto a las acciones tomadas y el ciclo
ggplot(datos, aes(x =ACTION_TAKEN_, y = CYCLES_BETWEEN_PET1_PET2))+
geom_bar(
aes(fill =CTCNCI ), stat = "identity", color = "white",
position = position_dodge(0.9)
)+
facet_wrap(~TREATMENT) +
fill_palette("jco")
# GRAFICO COMPARATIVO DE PACIENTES CON EFECTOS ADVERSOS SEGUN EL TRATAMIENTO Y ACCION DEL MEDICO
ggplot(datos) +
geom_bar(aes(x=ACTION_TAKEN_, fill=CTCNCI),
position = "dodge") +
facet_wrap(~TREATMENT)
# Histograma de densidad de DIFF_LIVER_UPTAKE por tratamiento:
var_x = 'DIFF_LIVER_UPTAKE'
name_x = as.name(var_x)
var_grupo = 'TREATMENT'
name_grupo = as.name(var_grupo)
options(warn=-1)
# With transparency (right)
p2 <- ggplot(data=datos, aes(x=!!name_x, group=!!name_grupo, fill=!!name_grupo)) +
geom_density(adjust=1.5, alpha=.4) +
theme_ipsum()
p2
ggplotly(p2)
options(warn=0)
# Mosaic plot y chi cuadrado de las variables cualitativas segun la variable respuesta:
r <- list()
for (i in 1:length(var_cual)) {
# Variable de salida o respuesta
name_y <- as.name(respuesta[1])
# Variable de entrada
name_i <- as.name(var_cual[i])
# Tabla de contingencias de una variable con la target
categ <- target_by(datos, !!name_y)
cat_cat <- relate(categ, !!name_i)
r[[i]]<-as.data.frame.matrix(cat_cat)
# Chi cuadrado
print(summary(cat_cat))
# Mosaico de una variable con la target
grid.arrange(plot(cat_cat),nrow=1, ncol = 1)
}
r
Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 1.4811, df = 1, p-value = 0.2236 Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 9.495, df = 3, p-value = 0.02338 Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 12.737, df = 9, p-value = 0.1749 Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 0.009083, df = 1, p-value = 0.9241
Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 5.748, df = 4, p-value = 0.2188 Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 5.938, df = 5, p-value = 0.3123 Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 2.7546, df = 4, p-value = 0.5997 Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE) Number of cases in table: 59 Number of factors: 2 Test for independence of all factors: Chisq = 13.937, df = 6, p-value = 0.03035 Chi-squared approximation may be incorrect
| FEMALE | MALE | |
|---|---|---|
| <int> | <int> | |
| ALIVE | 15 | 23 |
| DEATH | 5 | 16 |
| I | II | III | IV | |
|---|---|---|---|---|
| <int> | <int> | <int> | <int> | |
| ALIVE | 1 | 8 | 19 | 10 |
| DEATH | 5 | 7 | 7 | 2 |
| EWING SARCOMA | GASTRIC CANCER | GINECOLOGICAL | HEAD AND NECK | LUNG CANCER | MELANOMA | PANCREAS CANCER | RENAL CANCER | SARCOMA | UROTHELIAL CARCINOMA | |
|---|---|---|---|---|---|---|---|---|---|---|
| <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | |
| ALIVE | 2 | 0 | 6 | 6 | 14 | 2 | 4 | 3 | 0 | 1 |
| DEATH | 0 | 3 | 1 | 4 | 5 | 3 | 3 | 1 | 1 | 0 |
| CHEMO | ICI | |
|---|---|---|
| <int> | <int> | |
| ALIVE | 24 | 14 |
| DEATH | 13 | 8 |
| ASYMTOMATIC | BEDBOUND | SYMPTOMATIC >50 % IN THE BED | SYMPTOMATIC BUT AMBULATORY | SYMPTOMATIC,<50% IN BED DURING THE DAY | |
|---|---|---|---|---|---|
| <int> | <int> | <int> | <int> | <int> | |
| ALIVE | 19 | 0 | 1 | 8 | 10 |
| DEATH | 7 | 1 | 0 | 9 | 4 |
| ATEROESCLEROSIS | CANCER RRECURRENCE | CHRONIC INFLAMMATION | DIABETES MELLITUS | HYPERLIPIDEMIA | HYPERTENSION | |
|---|---|---|---|---|---|---|
| <int> | <int> | <int> | <int> | <int> | <int> | |
| ALIVE | 2 | 3 | 5 | 9 | 7 | 12 |
| DEATH | 1 | 5 | 4 | 4 | 5 | 2 |
| HIGHT SEVERE | MODERATE | NO SIDE EFFECTS | SEVERE | SLIGTHLY SIDE EFFCTS | |
|---|---|---|---|---|---|
| <int> | <int> | <int> | <int> | <int> | |
| ALIVE | 2 | 6 | 21 | 7 | 2 |
| DEATH | 2 | 5 | 7 | 5 | 2 |
| ADDED OTHER TREATMEN | ADDED STEROIDS | DOSE NOT CHANGED | DOSE REDUCED | DRUG INTERRUPTED | DRUG WIHDRAWN | UNKNOW | |
|---|---|---|---|---|---|---|---|
| <int> | <int> | <int> | <int> | <int> | <int> | <int> | |
| ALIVE | 3 | 0 | 17 | 9 | 3 | 0 | 6 |
| DEATH | 0 | 1 | 4 | 8 | 6 | 1 | 1 |
# Voy a definir el data frame de entrada: X
X = datos[-ncol(datos)]
head(X)
# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)
| STATUS | CYCLES_BETWEEN_PET1_PET2 | GENDER | AGE | TNM_STAGE | DIFF_WBC | DIFF_RBC | DIFF_HB | DIFF_PLT | DIFF_CRP | DIFF_ALBUMIN | DIFF_LDH | DIFF_eGFR | DIFF_AST | DIFF_ALT | DIFF_K | DIFF_BGL | BMI | DIFF_BW | DIFF_SPLEEN_UPTAKE | DIFF_BM_UPTAKE | DIFF_LIVER_UPTAKE | DIFF_ESTIMATED_SPLEEN_VOL | DIAGNOSTIC | TREATMENT | ECOGPS | COMORBIDITIES | CTCNCI | ACTION_TAKEN_ | TIME_BETWEE_PET | dias | DIFF_SLR | DIFF_BMLR |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <dbl> | <fct> | <dbl> | <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <fct> | <fct> | <fct> | <fct> | <fct> | <fct> | <dbl> | <dbl> | <dbl> | <dbl> |
| ALIVE | 3 | MALE | 54 | III | -1.83 | -0.64 | -2.20 | -82 | -0.56 | 0.98 | -271.0 | 23.7 | 0.3 | 13.2 | 0.10 | 12 | 22.90 | -4.0 | 0.02 | 0.12 | 0.44 | -80.9 | GINECOLOGICAL | ICI | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | SEVERE | DRUG INTERRUPTED | 2 | 25 | 0.029335236 | 0.10905937 |
| ALIVE | 2 | FEMALE | 79 | IV | -3.08 | 0.14 | 0.70 | -159 | -3.80 | 1.33 | 22.0 | -0.2 | 4.4 | 5.3 | 0.58 | -14 | 16.47 | 0.3 | -0.20 | -0.60 | -0.30 | 6.2 | HEAD AND NECK | ICI | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | DRUG INTERRUPTED | 4 | 0 | -0.156566790 | -0.04688961 |
| ALIVE | 3 | MALE | 60 | III | -3.18 | -0.50 | 3.54 | 22 | -0.06 | -0.20 | 64.2 | 4.6 | -2.6 | 1.1 | 0.33 | 12 | 20.15 | -8.1 | -0.12 | 0.34 | 0.27 | 9.1 | LUNG CANCER | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | DIABETES MELLITUS | MODERATE | DOSE REDUCED | 4 | 14 | -0.044698028 | -0.36568856 |
| ALIVE | 3 | MALE | 76 | II | -1.28 | 0.79 | 1.70 | 9 | 0.04 | -0.12 | -55.0 | -8.9 | -0.7 | -6.7 | 0.05 | -4 | 22.83 | 0.0 | 0.46 | -0.40 | 0.59 | -38.8 | RENAL CANCER | ICI | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | DOSE NOT CHANGED | 6 | 0 | -0.002886671 | 0.13061297 |
| ALIVE | 5 | FEMALE | 70 | II | 0.00 | -0.09 | -0.40 | -95 | -7.23 | 0.05 | 6.0 | -13.2 | 2.7 | 2.2 | 0.06 | -8 | 18.29 | -0.1 | -0.34 | -0.80 | -0.48 | -87.6 | MELANOMA | ICI | SYMPTOMATIC BUT AMBULATORY | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE NOT CHANGED | 6 | 0 | -0.095215760 | -0.25268025 |
| ALIVE | 4 | MALE | 54 | IV | 0.99 | 0.10 | 0.00 | -3 | 0.02 | -0.16 | -4.0 | -13.1 | 0.6 | 2.8 | -0.47 | -3 | 27.18 | -10.8 | 0.00 | -0.10 | -0.53 | 34.4 | LUNG CANCER | ICI | ASYMTOMATIC | CANCER RRECURRENCE | HIGHT SEVERE | DOSE NOT CHANGED | 6 | 363 | -0.167591661 | 0.07251379 |
#options(warn=-1)
## Multiple Wilcoxon rank sum tests - casos binarios
datos$STATUS <- as.numeric(datos$STATUS)
Wilk_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) wilcox.test(x ~ datos$STATUS)$p.value)))) %>%
arrange(V1)
Wilk_p
#options(warn=-0)
| V1 | |
|---|---|
| <dbl> | |
| DIFF_BW | 0.001071841 |
| DIFF_LIVER_UPTAKE | 0.052455399 |
| dias | 0.108429874 |
| DIFF_PLT | 0.115177643 |
| DIFF_CRP | 0.151877257 |
| OVERALL_TIME | 0.168110480 |
| BMI | 0.205329235 |
| DIFF_HB | 0.225794623 |
| DIFF_K | 0.244513380 |
| CYCLES_BETWEEN_PET1_PET2 | 0.250401922 |
| DIFF_WBC | 0.271201503 |
| AGE | 0.366288338 |
| DIFF_BMLR | 0.466463400 |
| DIFF_AST | 0.536942262 |
| DIFF_BGL | 0.557948763 |
| DIFF_SLR | 0.669052785 |
| DIFF_RBC | 0.680615800 |
| TIME_BETWEE_PET | 0.744938990 |
| DIFF_ALBUMIN | 0.769606349 |
| DIFF_ESTIMATED_SPLEEN_VOL | 0.771492426 |
| DIFF_LDH | 0.806140116 |
| DIFF_eGFR | 0.924323941 |
| DIFF_SPLEEN_UPTAKE | 0.974737237 |
| DIFF_BM_UPTAKE | 0.987368328 |
| DIFF_ALT | 1.000000000 |
## Kruskall wallis rank sum tests - para todos los casos
datos$STATUS <- as.numeric(datos$STATUS)
Kruskal_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) kruskal.test(x ~ datos$STATUS)$p.value)))) %>%
arrange(V1)
Kruskal_p
| V1 | |
|---|---|
| <dbl> | |
| DIFF_BW | 0.001042032 |
| DIFF_LIVER_UPTAKE | 0.051499589 |
| dias | 0.106574249 |
| DIFF_PLT | 0.113362723 |
| DIFF_CRP | 0.149567892 |
| OVERALL_TIME | 0.165679149 |
| BMI | 0.202511282 |
| DIFF_HB | 0.222742163 |
| DIFF_K | 0.241312102 |
| CYCLES_BETWEEN_PET1_PET2 | 0.247127164 |
| DIFF_WBC | 0.267761696 |
| AGE | 0.362099048 |
| DIFF_BMLR | 0.461632677 |
| DIFF_AST | 0.531723207 |
| DIFF_BGL | 0.552640237 |
| DIFF_SLR | 0.663298181 |
| DIFF_RBC | 0.674817935 |
| TIME_BETWEE_PET | 0.738942177 |
| DIFF_ALBUMIN | 0.763549184 |
| DIFF_ESTIMATED_SPLEEN_VOL | 0.763569297 |
| DIFF_LDH | 0.800017121 |
| DIFF_eGFR | 0.918037782 |
| DIFF_SPLEEN_UPTAKE | 0.968420824 |
| DIFF_BM_UPTAKE | 0.981053204 |
| DIFF_ALT | 0.993683966 |
Ahora hacemos la ANOVA que es el equivalente paramétrico a Kurskall Wallis
## ANOVA - para todos los casos
datos$STATUS <- as.factor(datos$STATUS)
ANOVA_p<-as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) anova(lm(x ~ datos$STATUS))$`Pr(>F)` [1])))) %>%
arrange(V1)
ANOVA_p
| V1 | |
|---|---|
| <dbl> | |
| DIFF_BW | 0.0008674499 |
| DIFF_LIVER_UPTAKE | 0.1128440197 |
| BMI | 0.1313002214 |
| DIFF_HB | 0.1987145533 |
| TIME_BETWEE_PET | 0.2006455075 |
| DIFF_ALT | 0.2283988495 |
| DIFF_WBC | 0.2702341457 |
| DIFF_PLT | 0.2858364966 |
| DIFF_K | 0.3178280254 |
| AGE | 0.3199523200 |
| OVERALL_TIME | 0.3872791867 |
| DIFF_BMLR | 0.4251613367 |
| dias | 0.4311242737 |
| DIFF_AST | 0.4411739211 |
| DIFF_CRP | 0.5579829726 |
| DIFF_BGL | 0.5739834861 |
| DIFF_LDH | 0.5836796913 |
| DIFF_eGFR | 0.5854167472 |
| DIFF_RBC | 0.6510598009 |
| DIFF_SLR | 0.6511375206 |
| CYCLES_BETWEEN_PET1_PET2 | 0.6804891959 |
| DIFF_ESTIMATED_SPLEEN_VOL | 0.7346993354 |
| DIFF_SPLEEN_UPTAKE | 0.8098195294 |
| DIFF_BM_UPTAKE | 0.9039677773 |
| DIFF_ALBUMIN | 0.9999543656 |
datos$STATUS<-as.numeric(datos$STATUS)
datos$GENDER <- as.numeric(datos$GENDER)
datos$COMORBIDITIES <- as.numeric(datos$COMORBIDITIES)
datos$CTCNCI <- as.numeric(datos$CTCNCI)
datos$ACTION_TAKEN_ <- as.numeric(datos$ACTION_TAKEN_)
datos$TNM_STAGE <- as.numeric(datos$TNM_STAGE)
datos$DIAGNOSTIC <- as.numeric(datos$DIAGNOSTIC)
datos$TREATMENT <- as.numeric(datos$TREATMENT)
datos$ECOGPS <- as.numeric(datos$ECOGPS)
res_sig <- rep(0,length(var_cual))
for (i in 1:length(var_cual)){
chisq <- chisq.test(table(as.factor(as.character(datos[,var_cual[i]])),datos[,'STATUS']),correct = TRUE,simulate.p.value = 10000)
res_sig[i]<-chisq$p.value
}
df_sig <- data.frame(p_valor=res_sig)
row.names(df_sig) <- colnames(datos[,var_cual])
idx_sig <- sort(df_sig$p_valor,decreasing = FALSE,index.return=TRUE)$ix
df_sig_sort <- data.frame(p_valor=res_sig[idx_sig])
row.names(df_sig_sort) <- colnames(datos[,var_cual])[idx_sig]
df_sig_sort
| p_valor | |
|---|---|
| <dbl> | |
| ACTION_TAKEN_ | 0.01399300 |
| TNM_STAGE | 0.02348826 |
| DIAGNOSTIC | 0.13593203 |
| ECOGPS | 0.18440780 |
| GENDER | 0.25187406 |
| COMORBIDITIES | 0.31534233 |
| CTCNCI | 0.67266367 |
| TREATMENT | 1.00000000 |
# Mutual informacion variables cuantitativas:
inf_gain <- information.gain(STATUS~., data = datos)
inf_gain <- inf_gain %>%
arrange(desc(attr_importance))
inf_gain
| attr_importance | |
|---|---|
| <dbl> | |
| TREATMENT | 0.4393713 |
| DIFF_BW | 0.2900315 |
| CYCLES_BETWEEN_PET1_PET2 | 0.0000000 |
| GENDER | 0.0000000 |
| AGE | 0.0000000 |
| TNM_STAGE | 0.0000000 |
| DIFF_WBC | 0.0000000 |
| DIFF_RBC | 0.0000000 |
| DIFF_HB | 0.0000000 |
| DIFF_PLT | 0.0000000 |
| DIFF_CRP | 0.0000000 |
| DIFF_ALBUMIN | 0.0000000 |
| DIFF_LDH | 0.0000000 |
| DIFF_eGFR | 0.0000000 |
| DIFF_AST | 0.0000000 |
| DIFF_ALT | 0.0000000 |
| DIFF_K | 0.0000000 |
| DIFF_BGL | 0.0000000 |
| BMI | 0.0000000 |
| DIFF_SPLEEN_UPTAKE | 0.0000000 |
| DIFF_BM_UPTAKE | 0.0000000 |
| DIFF_LIVER_UPTAKE | 0.0000000 |
| DIFF_ESTIMATED_SPLEEN_VOL | 0.0000000 |
| DIAGNOSTIC | 0.0000000 |
| ECOGPS | 0.0000000 |
| COMORBIDITIES | 0.0000000 |
| CTCNCI | 0.0000000 |
| ACTION_TAKEN_ | 0.0000000 |
| TIME_BETWEE_PET | 0.0000000 |
| dias | 0.0000000 |
| DIFF_SLR | 0.0000000 |
| DIFF_BMLR | 0.0000000 |
| OVERALL_TIME | 0.0000000 |
row.names(df_sig_sort)[1:3]
row.names(Kruskal_p)[1:5]
datos$STATUS<- as.factor(datos$STATUS)
modelos_5top <-
glmulti(STATUS~ACTION_TAKEN_+TNM_STAGE+DIAGNOSTIC+DIFF_BW+DIFF_LIVER_UPTAKE+dias+DIFF_PLT+DIFF_CRP, data = datos,
level = 1, # No interaction considered
method = "h", # Exhaustive approach
crit = "bic", # BIC as criteria
confsetsize = 5, # Keep 5 best models
plotty = F, report = F, # No plot or interim reports
fitfunction = "glm", # glm function
family = binomial) # binomial family for logistic regression
# Los 5 mejores modelos
print(modelos_5top@formulas)
options(warn = -1)
[[1]] STATUS ~ 1 + TNM_STAGE + DIFF_BW <environment: 0x000002432f554a78> [[2]] STATUS ~ 1 + DIFF_BW <environment: 0x000002432f554a78> [[3]] STATUS ~ 1 + TNM_STAGE + DIFF_BW + DIFF_LIVER_UPTAKE <environment: 0x000002432f554a78> [[4]] STATUS ~ 1 + TNM_STAGE + DIFF_BW + dias <environment: 0x000002432f554a78> [[5]] STATUS ~ 1 + TNM_STAGE + DIFF_BW + DIFF_PLT <environment: 0x000002432f554a78>
# El summary de los 5 mejores modelos
summary(modelos_5top@objects[[3]])
Call:
fitfunc(formula = as.formula(x), family = ..1, data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9267 -0.6980 -0.4480 0.8274 1.9488
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.23509 1.02726 1.202 0.2292
TNM_STAGE -0.83662 0.37196 -2.249 0.0245 *
DIFF_BW 0.11358 0.04968 2.286 0.0222 *
DIFF_LIVER_UPTAKE -0.86902 0.62713 -1.386 0.1658
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 76.823 on 58 degrees of freedom
Residual deviance: 57.697 on 55 degrees of freedom
AIC: 65.697
Number of Fisher Scoring iterations: 5
# Las variables que aparecen en el mejor modelo son:
var_sel_mod1<-names(modelos_5top@objects[[3]]$coefficients)[-1]
print(var_sel_mod1)
[1] "TNM_STAGE" "DIFF_BW" "DIFF_LIVER_UPTAKE"
var_sel_mod1 <- c("TNM_STAGE","DIFF_BW","DIFF_LIVER_UPTAKE")
var_sel_mod1
# Calculamos el mejor modelo y su accuracy:
# Construimos la formula de forma automatica:
x = paste(var_sel_mod1, collapse = " + ")
y = respuesta
formBIC = as.formula(paste(y, "~", x))
formBIC
STATUS ~ TNM_STAGE + DIFF_BW + DIFF_LIVER_UPTAKE
# Hacemos la matriz de confusion y los estadisticos del modelo
modelo1 <- train(formBIC, data=datos,method='glm')
print("Matriz de confusión")
pred <- predict(newdata=datos,modelo1) # prediccion usando el modelo logistico
real <- as.factor(datos[,respuesta]) # respuesta real
cm_train_TOT <- caret::confusionMatrix(data=pred,reference=real) # guardamos la matriz de confusion de training
print(cm_train_TOT) # pintamos la matriz de confusion de training
[1] "Matriz de confusión"
Confusion Matrix and Statistics
Reference
Prediction 1 2
1 33 12
2 5 9
Accuracy : 0.7119
95% CI : (0.5792, 0.8224)
No Information Rate : 0.6441
P-Value [Acc > NIR] : 0.1709
Kappa : 0.3209
Mcnemar's Test P-Value : 0.1456
Sensitivity : 0.8684
Specificity : 0.4286
Pos Pred Value : 0.7333
Neg Pred Value : 0.6429
Prevalence : 0.6441
Detection Rate : 0.5593
Detection Prevalence : 0.7627
Balanced Accuracy : 0.6485
'Positive' Class : 1
Una de las técnicas más completas que podemos aplicar es la metodología de Boruta.El problema es que el dataset tiene pocas observaciones y para que funcione perfectamente es mejor usar mas,pero lo aplicamos como comparacion.
# Voy a definir el data frame de entrada: X
X = datos[-1]
head(X)
# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)
| CYCLES_BETWEEN_PET1_PET2 | GENDER | AGE | TNM_STAGE | DIFF_WBC | DIFF_RBC | DIFF_HB | DIFF_PLT | DIFF_CRP | DIFF_ALBUMIN | DIFF_LDH | DIFF_eGFR | DIFF_AST | DIFF_ALT | DIFF_K | DIFF_BGL | BMI | DIFF_BW | DIFF_SPLEEN_UPTAKE | DIFF_BM_UPTAKE | DIFF_LIVER_UPTAKE | DIFF_ESTIMATED_SPLEEN_VOL | DIAGNOSTIC | TREATMENT | ECOGPS | COMORBIDITIES | CTCNCI | ACTION_TAKEN_ | TIME_BETWEE_PET | dias | DIFF_SLR | DIFF_BMLR | OVERALL_TIME |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <fct> | <dbl> | <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <fct> | <fct> | <fct> | <fct> | <fct> | <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| 3 | MALE | 54 | III | -1.83 | -0.64 | -2.20 | -82 | -0.56 | 0.98 | -271.0 | 23.7 | 0.3 | 13.2 | 0.10 | 12 | 22.90 | -4.0 | 0.02 | 0.12 | 0.44 | -80.9 | GINECOLOGICAL | ICI | SYMPTOMATIC BUT AMBULATORY | HYPERLIPIDEMIA | SEVERE | DRUG INTERRUPTED | 2 | 25 | 0.029335236 | 0.10905937 | 2 |
| 2 | FEMALE | 79 | IV | -3.08 | 0.14 | 0.70 | -159 | -3.80 | 1.33 | 22.0 | -0.2 | 4.4 | 5.3 | 0.58 | -14 | 16.47 | 0.3 | -0.20 | -0.60 | -0.30 | 6.2 | HEAD AND NECK | ICI | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | DRUG INTERRUPTED | 4 | 0 | -0.156566790 | -0.04688961 | 50 |
| 3 | MALE | 60 | III | -3.18 | -0.50 | 3.54 | 22 | -0.06 | -0.20 | 64.2 | 4.6 | -2.6 | 1.1 | 0.33 | 12 | 20.15 | -8.1 | -0.12 | 0.34 | 0.27 | 9.1 | LUNG CANCER | ICI | SYMPTOMATIC,<50% IN BED DURING THE DAY | DIABETES MELLITUS | MODERATE | DOSE REDUCED | 4 | 14 | -0.044698028 | -0.36568856 | 2 |
| 3 | MALE | 76 | II | -1.28 | 0.79 | 1.70 | 9 | 0.04 | -0.12 | -55.0 | -8.9 | -0.7 | -6.7 | 0.05 | -4 | 22.83 | 0.0 | 0.46 | -0.40 | 0.59 | -38.8 | RENAL CANCER | ICI | ASYMTOMATIC | HYPERTENSION | NO SIDE EFFECTS | DOSE NOT CHANGED | 6 | 0 | -0.002886671 | 0.13061297 | 12 |
| 5 | FEMALE | 70 | II | 0.00 | -0.09 | -0.40 | -95 | -7.23 | 0.05 | 6.0 | -13.2 | 2.7 | 2.2 | 0.06 | -8 | 18.29 | -0.1 | -0.34 | -0.80 | -0.48 | -87.6 | MELANOMA | ICI | SYMPTOMATIC BUT AMBULATORY | DIABETES MELLITUS | NO SIDE EFFECTS | DOSE NOT CHANGED | 6 | 0 | -0.095215760 | -0.25268025 | 12 |
| 4 | MALE | 54 | IV | 0.99 | 0.10 | 0.00 | -3 | 0.02 | -0.16 | -4.0 | -13.1 | 0.6 | 2.8 | -0.47 | -3 | 27.18 | -10.8 | 0.00 | -0.10 | -0.53 | 34.4 | LUNG CANCER | ICI | ASYMTOMATIC | CANCER RRECURRENCE | HIGHT SEVERE | DOSE NOT CHANGED | 6 | 363 | -0.167591661 | 0.07251379 | 18 |
# Determinar la importancia de los atributos.
boruta.model <- Boruta(y~., data = cbind(X,y), doTrace = 2)
1. run of importance source... 2. run of importance source... 3. run of importance source... 4. run of importance source... 5. run of importance source... 6. run of importance source... 7. run of importance source... 8. run of importance source... 9. run of importance source... 10. run of importance source... 11. run of importance source... 12. run of importance source... After 12 iterations, +0.53 secs: rejected 30 attributes: ACTION_TAKEN_, AGE, BMI, COMORBIDITIES, CTCNCI and 25 more; still have 3 attributes left. 13. run of importance source... 14. run of importance source... 15. run of importance source... 16. run of importance source... 17. run of importance source... 18. run of importance source... 19. run of importance source... 20. run of importance source... After 20 iterations, +0.79 secs: confirmed 1 attribute: DIFF_BW; still have 2 attributes left. 21. run of importance source... 22. run of importance source... 23. run of importance source... 24. run of importance source... 25. run of importance source... 26. run of importance source... 27. run of importance source... 28. run of importance source... 29. run of importance source... 30. run of importance source... 31. run of importance source... 32. run of importance source... 33. run of importance source... 34. run of importance source... 35. run of importance source... 36. run of importance source... 37. run of importance source... 38. run of importance source... 39. run of importance source... 40. run of importance source... 41. run of importance source... 42. run of importance source... 43. run of importance source... 44. run of importance source... 45. run of importance source... 46. run of importance source... 47. run of importance source... 48. run of importance source... 49. run of importance source... 50. run of importance source... 51. run of importance source... 52. run of importance source... 53. run of importance source... 54. run of importance source... 55. run of importance source... 56. run of importance source... 57. run of importance source... 58. run of importance source... 59. run of importance source... 60. run of importance source... 61. run of importance source... 62. run of importance source... 63. run of importance source... 64. run of importance source... 65. run of importance source... 66. run of importance source... 67. run of importance source... 68. run of importance source... 69. run of importance source... 70. run of importance source... 71. run of importance source... 72. run of importance source... 73. run of importance source... 74. run of importance source... 75. run of importance source... 76. run of importance source... 77. run of importance source... 78. run of importance source... 79. run of importance source... 80. run of importance source... 81. run of importance source... 82. run of importance source... 83. run of importance source... 84. run of importance source... 85. run of importance source... 86. run of importance source... 87. run of importance source... 88. run of importance source... 89. run of importance source... 90. run of importance source... After 90 iterations, +2.8 secs: confirmed 1 attribute: TNM_STAGE; still have 1 attribute left. 91. run of importance source... 92. run of importance source... 93. run of importance source... 94. run of importance source... 95. run of importance source... 96. run of importance source... 97. run of importance source... 98. run of importance source... 99. run of importance source...
options(repr.plot.width=12, repr.plot.height=8)
print(boruta.model)
plot(boruta.model)
Boruta performed 99 iterations in 3.069594 secs. 2 attributes confirmed important: DIFF_BW, TNM_STAGE; 30 attributes confirmed unimportant: ACTION_TAKEN_, AGE, BMI, COMORBIDITIES, CTCNCI and 25 more; 1 tentative attributes left: DIFF_CRP;
# Refinar modelo para resolver posibles atributos tentativos.
# Dado que pueden existir atributos no resueltos (tentativos), refinamos el modelo.
boruta.model2 <- TentativeRoughFix(boruta.model)
print(boruta.model2)
plot(boruta.model2)
Boruta performed 99 iterations in 3.069594 secs. Tentatives roughfixed over the last 99 iterations. 2 attributes confirmed important: DIFF_BW, TNM_STAGE; 31 attributes confirmed unimportant: ACTION_TAKEN_, AGE, BMI, COMORBIDITIES, CTCNCI and 26 more;
# Obtener una lista de los atributos y sus etiquetas con el analisis de Boruta
as.data.frame(boruta.model$finalDecision)
| boruta.model$finalDecision | |
|---|---|
| <fct> | |
| CYCLES_BETWEEN_PET1_PET2 | Rejected |
| GENDER | Rejected |
| AGE | Rejected |
| TNM_STAGE | Confirmed |
| DIFF_WBC | Rejected |
| DIFF_RBC | Rejected |
| DIFF_HB | Rejected |
| DIFF_PLT | Rejected |
| DIFF_CRP | Tentative |
| DIFF_ALBUMIN | Rejected |
| DIFF_LDH | Rejected |
| DIFF_eGFR | Rejected |
| DIFF_AST | Rejected |
| DIFF_ALT | Rejected |
| DIFF_K | Rejected |
| DIFF_BGL | Rejected |
| BMI | Rejected |
| DIFF_BW | Confirmed |
| DIFF_SPLEEN_UPTAKE | Rejected |
| DIFF_BM_UPTAKE | Rejected |
| DIFF_LIVER_UPTAKE | Rejected |
| DIFF_ESTIMATED_SPLEEN_VOL | Rejected |
| DIAGNOSTIC | Rejected |
| TREATMENT | Rejected |
| ECOGPS | Rejected |
| COMORBIDITIES | Rejected |
| CTCNCI | Rejected |
| ACTION_TAKEN_ | Rejected |
| TIME_BETWEE_PET | Rejected |
| dias | Rejected |
| DIFF_SLR | Rejected |
| DIFF_BMLR | Rejected |
| OVERALL_TIME | Rejected |
# Obtener una lista de los atributos importantes y tentativos
opc_boruta = getSelectedAttributes(boruta.model2, withTentative = F)
opc_boruta
# Construimos la formula de forma automatica:
x = paste(opc_boruta, collapse = " + ")
y = respuesta
formBoruta = as.formula(paste(y, "~", x))
formBoruta
STATUS ~ TNM_STAGE + DIFF_BW
library(ROCR)
index = sample(1:nrow(datos), size = .100 * nrow(datos))
#index
train = datos[index, ]
test = datos[-index, ]
model = glm(STATUS~ACTION_TAKEN_+TNM_STAGE+DIAGNOSTIC+DIFF_BW+DIFF_LIVER_UPTAKE+dias+DIFF_PLT+DIFF_CRP,data=train,
family = binomial(link = "logit"))
pred = predict(model,test,type="response")
pred = prediction(pred,test$STATUS)
perf = performance(pred, "acc")
#plot(perf)
max_ind = which.max(slot(perf, "y.values")[[1]] )
acc = slot(perf, "y.values")[[1]][max_ind]
cutoff = slot(perf, "x.values")[[1]][max_ind]
print(c(accuracy= acc))
perf_cost = performance(pred, "cost")
perf_err = performance(pred, "err")
perf_tpr = performance(pred, "tpr")
perf_sn_sp = performance(pred, "sens", "spec")
roc = performance(pred,"tpr","fpr")
plot(roc, colorize = T, lwd = 2)
abline(a = 0, b = 1)
auc = performance(pred, measure = "auc")
print(auc@y.values)
accuracy 0.7222222 [[1]] [1] 0.7022556
El principal problema que nos hemos encontrado es que es un dataset con pocas observaciones.Aunque la estadistica inferencial procura sacar conclusiones generales a traves de una muestra , esta debe ser lo suficientemente significativa para que los distintos test y modelos puedan hacer su trabajo con un minimo de confiabilidad y minuciosidad.
A pesar de ello hemos sacado algunas conclusiones que quiza no sean importantes o relevantes en la medida de lo que queriamos.
Pero por otro lado observando con minuciosidad los distintos graficos comparativos si podemos extraer conclusiones mas valiosas.